Scheduled Commit

data/retrieval_battle-f0f24c65-6f65-4435-a1e5-20bb12a2b295.jsonl

@@ -54,3 +54,17 @@
 {"tstamp": 1722226993.9858, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "c966ef1f66124006834ce9fae7ec6c57", "0_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "0_prompt": "Adversarial example generation for text classification using BERT masked language model  ", "0_output": [["Adversarial example generation for text classification using BERT masked language model  ", "Title: Measuring Adversarial Datasets\n\nAbstract: In the era of widespread public use of AI systems across various domains, ensuring adversarial robustness has become increasingly vital to maintain safety and prevent undesirable errors. Researchers have curated various adversarial datasets (through perturbations) for capturing model deficiencies that cannot be revealed in standard benchmark datasets. However, little is known about how these adversarial examples differ from the original data points, and there is still no methodology to measure the intended and unintended consequences of those adversarial transformations. In this research, we conducted a systematic survey of existing quantifiable metrics that describe text instances in NLP tasks, among dimensions of difficulty, diversity, and disagreement. We selected several current adversarial effect datasets and compared the distributions between the original and their adversarial counterparts. The results provide valuable insights into what makes these datasets more challenging from a metrics perspective and whether they align with underlying assumptions."]], "0_corpus": "arxiv", "1_conv_id": "275ee03e6e634f92968096b192b9ae4a", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": "Adversarial example generation for text classification using BERT masked language model  ", "1_output": [["Adversarial example generation for text classification using BERT masked language model  ", "Title: Similar glassy features in the NMR response of pure and disordered La1.88Sr0.12CuO4\n\nAbstract: High Tc superconductivity in La2-xSrxCuO4 coexists with (striped and glassy) magnetic order. Here, we report NMR measurements of the 139La spin-lattice relaxation, which displays a stretched-exponential time dependence, in both pure and disordered x=0.12 single crystals. An analysis in terms of a distribution of relaxation rates T1^-1 indicates that i) the spin-freezing temperature is spatially inhomogeneous with an onset at Tg(onset)=20 K for the pristine samples, and ii) the width of the T1^-1 distribution in the vicinity of Tg(onset) is insensitive to an ~1% level of atomic disorder in CuO2 planes. This suggests that the stretched-exponential 139La relaxation, considered as a manifestation of the systems glassiness, may not arise from quenched disorder."]], "1_corpus": "arxiv"}
 {"tstamp": 1722227007.968, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "73fda287fa15475a92a663d6a3dba7cb", "0_model_name": "mixedbread-ai/mxbai-embed-large-v1", "0_prompt": "Achieving high accuracy in English-French plagiarism detection using word embeddings", "0_output": [["Achieving high accuracy in English-French plagiarism detection using word embeddings", "Title: UsingWord Embedding for Cross-Language Plagiarism Detection\n\nAbstract: This paper proposes to use distributed representation of words (word embeddings) in cross-language textual similarity detection. The main contributions of this paper are the following: (a) we introduce new cross-language similarity detection methods based on distributed representation of words; (b) we combine the different methods proposed to verify their complementarity and finally obtain an overall F1 score of 89.15% for English-French similarity detection at chunk level (88.5% at sentence level) on a very challenging corpus."]], "0_corpus": "arxiv", "1_conv_id": "339520347d484e1c8068e44e4e4e7452", "1_model_name": "jinaai/jina-embeddings-v2-base-en", "1_prompt": "Achieving high accuracy in English-French plagiarism detection using word embeddings", "1_output": [["Achieving high accuracy in English-French plagiarism detection using word embeddings", "Title: Studies of Plasma Detachment Using a One Dimensional Model for Divertor Operation\n\nAbstract: To characterize the conditions required to reach advanced divertor regimes, a one-dimensional computational model has been developed based on a coordinate transformation to incorporate two-dimensional effects. This model includes transport of ions, two species each of atoms and molecules, momentum, and ion and electron energy both within and across the flux surfaces. Impurity radiation is calculated using a coronal equilibrium model which includes the effects of charge-exchange recombination. Numerical results indicate that impurity radiation acts to facilitate plasma detachment and enhances the power lost from the divertor channel in escaping neutral atoms by cooling the electrons and suppressing ionization. As divertor particle densities increase, cold and thermal molecules become increasingly important in cooling the plasma, with molecular densities dominating electron and atomic densities under some conditions."]], "1_corpus": "arxiv"}
 {"tstamp": 1722227015.1404, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "de8d07d9dc434154b214bde3478b3319", "0_model_name": "BM25", "0_prompt": "A paper presenting a Transformer model for vision-and-language tasks that does not rely on object detection or ResNet.", "0_output": [["A paper presenting a Transformer model for vision-and-language tasks that does not rely on object detection or ResNet.", "Title: ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision\n\nAbstract: Vision-and-Language Pre-training (VLP) has improved performance on various joint vision-and-language downstream tasks. Current approaches to VLP heavily rely on image feature extraction processes, most of which involve region supervision (e.g., object detection) and the convolutional architecture (e.g., ResNet). Although disregarded in the literature, we find it problematic in terms of both (1) efficiency/speed, that simply extracting input features requires much more computation than the multimodal interaction steps; and (2) expressive power, as it is upper bounded to the expressive power of the visual embedder and its predefined visual vocabulary. In this paper, we present a minimal VLP model, Vision-and-Language Transformer (ViLT), monolithic in the sense that the processing of visual inputs is drastically simplified to just the same convolution-free manner that we process textual inputs. We show that ViLT is up to tens of times faster than previous VLP models, yet with competitive or better downstream task performance. Our code and pre-trained weights are available at https://github.com/dandelin/vilt."]], "0_corpus": "arxiv", "1_conv_id": "21bf134438c34376b884b388bf7b8c19", "1_model_name": "intfloat/e5-mistral-7b-instruct", "1_prompt": "A paper presenting a Transformer model for vision-and-language tasks that does not rely on object detection or ResNet.", "1_output": [["A paper presenting a Transformer model for vision-and-language tasks that does not rely on object detection or ResNet.", "Title: ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision\n\nAbstract: Vision-and-Language Pre-training (VLP) has improved performance on various joint vision-and-language downstream tasks. Current approaches to VLP heavily rely on image feature extraction processes, most of which involve region supervision (e.g., object detection) and the convolutional architecture (e.g., ResNet). Although disregarded in the literature, we find it problematic in terms of both (1) efficiency/speed, that simply extracting input features requires much more computation than the multimodal interaction steps; and (2) expressive power, as it is upper bounded to the expressive power of the visual embedder and its predefined visual vocabulary. In this paper, we present a minimal VLP model, Vision-and-Language Transformer (ViLT), monolithic in the sense that the processing of visual inputs is drastically simplified to just the same convolution-free manner that we process textual inputs. We show that ViLT is up to tens of times faster than previous VLP models, yet with competitive or better downstream task performance. Our code and pre-trained weights are available at https://github.com/dandelin/vilt."]], "1_corpus": "arxiv"}
+{"tstamp": 1722227022.9101, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "13b46854dae44ccfa5b050966e192eba", "0_model_name": "text-embedding-004", "0_prompt": "How do explanations in context influence the performance of language models?", "0_output": [["How do explanations in context influence the performance of language models?", "Title: Can language models learn from explanations in context?\n\nAbstract: Language Models (LMs) can perform new tasks by adapting to a few in-context examples. For humans, explanations that connect examples to task principles can improve learning. We therefore investigate whether explanations of few-shot examples can help LMs. We annotate questions from 40 challenging tasks with answer explanations, and various matched control explanations. We evaluate how different types of explanations, instructions, and controls affect zero- and few-shot performance. We analyze these results using statistical multilevel modeling techniques that account for the nested dependencies among conditions, tasks, prompts, and models. We find that explanations can improve performance -- even without tuning. Furthermore, explanations hand-tuned for performance on a small validation set offer substantially larger benefits, and building a prompt by selecting examples and explanations together substantially improves performance over selecting examples alone. Finally, even untuned explanations outperform carefully matched controls, suggesting that the benefits are due to the link between an example and its explanation, rather than lower-level features. However, only large models benefit. In summary, explanations can support the in-context learning of large LMs on challenging tasks."]], "0_corpus": "arxiv", "1_conv_id": "f0f4c01bfc3f47babe5c73dfa7135da2", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "How do explanations in context influence the performance of language models?", "1_output": [["How do explanations in context influence the performance of language models?", "Title: Can language models learn from explanations in context?\n\nAbstract: Language Models (LMs) can perform new tasks by adapting to a few in-context examples. For humans, explanations that connect examples to task principles can improve learning. We therefore investigate whether explanations of few-shot examples can help LMs. We annotate questions from 40 challenging tasks with answer explanations, and various matched control explanations. We evaluate how different types of explanations, instructions, and controls affect zero- and few-shot performance. We analyze these results using statistical multilevel modeling techniques that account for the nested dependencies among conditions, tasks, prompts, and models. We find that explanations can improve performance -- even without tuning. Furthermore, explanations hand-tuned for performance on a small validation set offer substantially larger benefits, and building a prompt by selecting examples and explanations together substantially improves performance over selecting examples alone. Finally, even untuned explanations outperform carefully matched controls, suggesting that the benefits are due to the link between an example and its explanation, rather than lower-level features. However, only large models benefit. In summary, explanations can support the in-context learning of large LMs on challenging tasks."]], "1_corpus": "arxiv"}
+{"tstamp": 1722227028.8236, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "ce9a309e5ad24ba38deee7ff08db73eb", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_prompt": "Study introducing a BERT-based framework achieving superior performance on scalable DST benchmarks like Sim-M and Sim-R.", "0_output": [["Study introducing a BERT-based framework achieving superior performance on scalable DST benchmarks like Sim-M and Sim-R.", "Title: BERT-DST: Scalable End-to-End Dialogue State Tracking with Bidirectional Encoder Representations from Transformer\n\nAbstract: An important yet rarely tackled problem in dialogue state tracking (DST) is scalability for dynamic ontology (e.g., movie, restaurant) and unseen slot values. We focus on a specific condition, where the ontology is unknown to the state tracker, but the target slot value (except for none and dontcare), possibly unseen during training, can be found as word segment in the dialogue context. Prior approaches often rely on candidate generation from n-gram enumeration or slot tagger outputs, which can be inefficient or suffer from error propagation. We propose BERT-DST, an end-to-end dialogue state tracker which directly extracts slot values from the dialogue context. We use BERT as dialogue context encoder whose contextualized language representations are suitable for scalable DST to identify slot values from their semantic context. Furthermore, we employ encoder parameter sharing across all slots with two advantages: (1) Number of parameters does not grow linearly with the ontology. (2) Language representation knowledge can be transferred among slots. Empirical evaluation shows BERT-DST with cross-slot parameter sharing outperforms prior work on the benchmark scalable DST datasets Sim-M and Sim-R, and achieves competitive performance on the standard DSTC2 and WOZ 2.0 datasets."]], "0_corpus": "arxiv", "1_conv_id": "fa33616b16934385bfd92cd0a0c413cf", "1_model_name": "text-embedding-3-large", "1_prompt": "Study introducing a BERT-based framework achieving superior performance on scalable DST benchmarks like Sim-M and Sim-R.", "1_output": [["Study introducing a BERT-based framework achieving superior performance on scalable DST benchmarks like Sim-M and Sim-R.", "Title: BERT-DST: Scalable End-to-End Dialogue State Tracking with Bidirectional Encoder Representations from Transformer\n\nAbstract: An important yet rarely tackled problem in dialogue state tracking (DST) is scalability for dynamic ontology (e.g., movie, restaurant) and unseen slot values. We focus on a specific condition, where the ontology is unknown to the state tracker, but the target slot value (except for none and dontcare), possibly unseen during training, can be found as word segment in the dialogue context. Prior approaches often rely on candidate generation from n-gram enumeration or slot tagger outputs, which can be inefficient or suffer from error propagation. We propose BERT-DST, an end-to-end dialogue state tracker which directly extracts slot values from the dialogue context. We use BERT as dialogue context encoder whose contextualized language representations are suitable for scalable DST to identify slot values from their semantic context. Furthermore, we employ encoder parameter sharing across all slots with two advantages: (1) Number of parameters does not grow linearly with the ontology. (2) Language representation knowledge can be transferred among slots. Empirical evaluation shows BERT-DST with cross-slot parameter sharing outperforms prior work on the benchmark scalable DST datasets Sim-M and Sim-R, and achieves competitive performance on the standard DSTC2 and WOZ 2.0 datasets."]], "1_corpus": "arxiv"}
+{"tstamp": 1722227065.5016, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "d4f40214f39349929660960ef995c744", "0_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "0_prompt": "A study showing the drawbacks of using standard MLE training and offering a new training procedure for better test performance in language models.", "0_output": [["A study showing the drawbacks of using standard MLE training and offering a new training procedure for better test performance in language models.", "Title: Recurrent Neural Language Models as Probabilistic Finite-state Automata\n\nAbstract: Studying language models (LMs) in terms of well-understood formalisms allows us to precisely characterize their abilities and limitations. Previous work has investigated the representational capacity of recurrent neural network (RNN) LMs in terms of their capacity to recognize unweighted formal languages. However, LMs do not describe unweighted formal languages -- rather, they define \\emph{probability distributions} over strings. In this work, we study what classes of such probability distributions RNN LMs can represent, which allows us to make more direct statements about their capabilities. We show that simple RNNs are equivalent to a subclass of probabilistic finite-state automata, and can thus model a strict subset of probability distributions expressible by finite-state models. Furthermore, we study the space complexity of representing finite-state LMs with RNNs. We show that, to represent an arbitrary deterministic finite-state LM with $N$ states over an alphabet $\\alphabet$, an RNN requires $\\Omega\\left(N |\\Sigma|\\right)$ neurons. These results present a first step towards characterizing the classes of distributions RNN LMs can represent and thus help us understand their capabilities and limitations."]], "0_corpus": "arxiv", "1_conv_id": "d39dbe79ef5d443683896e332508c895", "1_model_name": "sentence-transformers/all-MiniLM-L6-v2", "1_prompt": "A study showing the drawbacks of using standard MLE training and offering a new training procedure for better test performance in language models.", "1_output": [["A study showing the drawbacks of using standard MLE training and offering a new training procedure for better test performance in language models.", "Title: Neural Architecture Search as Sparse Supernet\n\nAbstract: This paper aims at enlarging the problem of Neural Architecture Search (NAS) from Single-Path and Multi-Path Search to automated Mixed-Path Search. In particular, we model the NAS problem as a sparse supernet using a new continuous architecture representation with a mixture of sparsity constraints. The sparse supernet enables us to automatically achieve sparsely-mixed paths upon a compact set of nodes. To optimize the proposed sparse supernet, we exploit a hierarchical accelerated proximal gradient algorithm within a bi-level optimization framework. Extensive experiments on Convolutional Neural Network and Recurrent Neural Network search demonstrate that the proposed method is capable of searching for compact, general and powerful neural architectures."]], "1_corpus": "arxiv"}
+{"tstamp": 1722227074.3205, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "ec5eb017dc4d4d9fa6d04d114fcc2e00", "0_model_name": "BAAI/bge-large-en-v1.5", "0_prompt": "Propaganda-as-a-service via training-time attacks on language models for biased text generation  ", "0_output": [["Propaganda-as-a-service via training-time attacks on language models for biased text generation  ", "Title: Spinning Language Models: Risks of Propaganda-As-A-Service and Countermeasures\n\nAbstract: We investigate a new threat to neural sequence-to-sequence (seq2seq) models: training-time attacks that cause models to \"spin\" their outputs so as to support an adversary-chosen sentiment or point of view -- but only when the input contains adversary-chosen trigger words. For example, a spinned summarization model outputs positive summaries of any text that mentions the name of some individual or organization. Model spinning introduces a \"meta-backdoor\" into a model. Whereas conventional backdoors cause models to produce incorrect outputs on inputs with the trigger, outputs of spinned models preserve context and maintain standard accuracy metrics, yet also satisfy a meta-task chosen by the adversary. Model spinning enables propaganda-as-a-service, where propaganda is defined as biased speech. An adversary can create customized language models that produce desired spins for chosen triggers, then deploy these models to generate disinformation (a platform attack), or else inject them into ML training pipelines (a supply-chain attack), transferring malicious functionality to downstream models trained by victims. To demonstrate the feasibility of model spinning, we develop a new backdooring technique. It stacks an adversarial meta-task onto a seq2seq model, backpropagates the desired meta-task output to points in the word-embedding space we call \"pseudo-words,\" and uses pseudo-words to shift the entire output distribution of the seq2seq model. We evaluate this attack on language generation, summarization, and translation models with different triggers and meta-tasks such as sentiment, toxicity, and entailment. Spinned models largely maintain their accuracy metrics (ROUGE and BLEU) while shifting their outputs to satisfy the adversary's meta-task. We also show that, in the case of a supply-chain attack, the spin functionality transfers to downstream models."]], "0_corpus": "arxiv", "1_conv_id": "009892afcd5f438aa105fea295c61e62", "1_model_name": "GritLM/GritLM-7B", "1_prompt": "Propaganda-as-a-service via training-time attacks on language models for biased text generation  ", "1_output": [["Propaganda-as-a-service via training-time attacks on language models for biased text generation  ", "Title: Spinning Language Models: Risks of Propaganda-As-A-Service and Countermeasures\n\nAbstract: We investigate a new threat to neural sequence-to-sequence (seq2seq) models: training-time attacks that cause models to \"spin\" their outputs so as to support an adversary-chosen sentiment or point of view -- but only when the input contains adversary-chosen trigger words. For example, a spinned summarization model outputs positive summaries of any text that mentions the name of some individual or organization. Model spinning introduces a \"meta-backdoor\" into a model. Whereas conventional backdoors cause models to produce incorrect outputs on inputs with the trigger, outputs of spinned models preserve context and maintain standard accuracy metrics, yet also satisfy a meta-task chosen by the adversary. Model spinning enables propaganda-as-a-service, where propaganda is defined as biased speech. An adversary can create customized language models that produce desired spins for chosen triggers, then deploy these models to generate disinformation (a platform attack), or else inject them into ML training pipelines (a supply-chain attack), transferring malicious functionality to downstream models trained by victims. To demonstrate the feasibility of model spinning, we develop a new backdooring technique. It stacks an adversarial meta-task onto a seq2seq model, backpropagates the desired meta-task output to points in the word-embedding space we call \"pseudo-words,\" and uses pseudo-words to shift the entire output distribution of the seq2seq model. We evaluate this attack on language generation, summarization, and translation models with different triggers and meta-tasks such as sentiment, toxicity, and entailment. Spinned models largely maintain their accuracy metrics (ROUGE and BLEU) while shifting their outputs to satisfy the adversary's meta-task. We also show that, in the case of a supply-chain attack, the spin functionality transfers to downstream models."]], "1_corpus": "arxiv"}
+{"tstamp": 1722227089.1997, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "a76136d3818e49c29e4baa8391ebbab2", "0_model_name": "sentence-transformers/all-MiniLM-L6-v2", "0_prompt": "Research on the effectiveness of chain-of-thought prompting to solve difficult language model tasks", "0_output": [["Research on the effectiveness of chain-of-thought prompting to solve difficult language model tasks", "Title: System-Level Genetic Codes: An Explanation for Biological Complexity\n\nAbstract: Complex systems with tightly coadapted parts frequently appear in living systems and are difficult to account for through Darwinian evolution, that is random variation and natural selection, if the constituent parts are independently coded in the genetic code. If the parts are independently coded, multiple simultaneous mutations appear necessary to create or modify these systems. It is generally believed that most proteins are independently coded. The textbook rule is one gene for one enzyme. Thus, biochemical systems with tightly coadapted parts such as the blood clotting cascade pose a difficulty for Darwinian evolution. This problem can be overcome if the current understanding of the genetic code is incomplete and a system-level genetic code in which seemingly independent proteins are encoded in an interdependent, highly correlated manner exists. The methods by which human beings design and fabricate complex systems of tightly coadapted parts are explored for insights into the requirements for a system-level genetic code. Detailed examples of system-level codes for networks of matching parts are presented. The implications of identifying and deciphering the system-level genetic code if it exists for the prevention, treatment, and cure of heart disease, cancer, immune disorders, and for rational drug design are discussed."]], "0_corpus": "arxiv", "1_conv_id": "891e5ee1250b4d1fac7f62baa9315f5e", "1_model_name": "intfloat/e5-mistral-7b-instruct", "1_prompt": "Research on the effectiveness of chain-of-thought prompting to solve difficult language model tasks", "1_output": [["Research on the effectiveness of chain-of-thought prompting to solve difficult language model tasks", "Title: ChainLM: Empowering Large Language Models with Improved Chain-of-Thought Prompting\n\nAbstract: Chain-of-Thought (CoT) prompting can enhance the reasoning capabilities of large language models (LLMs), establishing itself as a primary approach to solving complex reasoning tasks. Existing CoT synthesis approaches usually focus on simpler reasoning tasks and thus result in low-quality and inconsistent CoT prompts. In response to this challenge, we present an empirical investigation of CoT prompting and introduce CoTGenius, a novel framework designed for the automatic generation of superior CoT prompts. CoTGenius is developed based on three major evolution strategies, i.e., complicate, diversify, and specify-alongside two filtering mechanisms: evolutionary success judgement and correctness verification. We further employ CoTGenius to create an extensive CoT dataset, and subsequently fine-tune the Llama 2-Chat 7B and 13B models on this dataset. We call the resulting model ChainLM. To deal with the cumulative error issue in reasoning steps, we propose a step-level debating method, wherein multiple debaters discuss each reasoning step to arrive at the correct answer. Extensive experiments demonstrate that our ChainLM models exhibit enhanced proficiency in addressing a spectrum of complex reasoning problems compared to existing models. In addition, we conduct an in-depth analysis of the impact of data categories within CoTGenius on the model performance. We release our dataset and code at https://github.com/RUCAIBox/ChainLM."]], "1_corpus": "arxiv"}
+{"tstamp": 1722227103.829, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "0b5c946bf2ff4358b48d8d4a1a8e9854", "0_model_name": "sentence-transformers/all-MiniLM-L6-v2", "0_prompt": "Study analyzing how well large language models can assist students in debugging code.", "0_output": [["Study analyzing how well large language models can assist students in debugging code.", "Title: A characterization of quasipositive Seifert surfaces (Constructions of quasipositive knots and links, III)\n\nAbstract: This article was originally published in Topology 31 (1992). The present hyperTeXed redaction corrects a few typographical errors and updates the references."]], "0_corpus": "arxiv", "1_conv_id": "721c802e8f3b4f46846d3f26b194aee4", "1_model_name": "intfloat/multilingual-e5-large-instruct", "1_prompt": "Study analyzing how well large language models can assist students in debugging code.", "1_output": [["Study analyzing how well large language models can assist students in debugging code.", "Title: Exploring the Responses of Large Language Models to Beginner Programmers' Help Requests\n\nAbstract: Background and Context: Over the past year, large language models (LLMs) have taken the world by storm. In computing education, like in other walks of life, many opportunities and threats have emerged as a consequence. Objectives: In this article, we explore such opportunities and threats in a specific area: responding to student programmers' help requests. More specifically, we assess how good LLMs are at identifying issues in problematic code that students request help on. Method: We collected a sample of help requests and code from an online programming course. We then prompted two different LLMs (OpenAI Codex and GPT-3.5) to identify and explain the issues in the students' code and assessed the LLM-generated answers both quantitatively and qualitatively. Findings: GPT-3.5 outperforms Codex in most respects. Both LLMs frequently find at least one actual issue in each student program (GPT-3.5 in 90% of the cases). Neither LLM excels at finding all the issues (GPT-3.5 finding them 57% of the time). False positives are common (40% chance for GPT-3.5). The advice that the LLMs provide on the issues is often sensible. The LLMs perform better on issues involving program logic rather than on output formatting. Model solutions are frequently provided even when the LLM is prompted not to. LLM responses to prompts in a non-English language are only slightly worse than responses to English prompts. Implications: Our results continue to highlight the utility of LLMs in programming education. At the same time, the results highlight the unreliability of LLMs: LLMs make some of the same mistakes that students do, perhaps especially when formatting output as required by automated assessment systems. Our study informs teachers interested in using LLMs as well as future efforts to customize LLMs for the needs of programming education."]], "1_corpus": "arxiv"}
+{"tstamp": 1722227110.4423, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "afa919e9ef4546cea9fd3213076acaca", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "Metrics for evaluating step-by-step reasoning in language models", "0_output": [["Metrics for evaluating step-by-step reasoning in language models", "Title: ROSCOE: A Suite of Metrics for Scoring Step-by-Step Reasoning\n\nAbstract: Large language models show improved downstream task performance when prompted to generate step-by-step reasoning to justify their final answers. These reasoning steps greatly improve model interpretability and verification, but objectively studying their correctness (independent of the final answer) is difficult without reliable methods for automatic evaluation. We simply do not know how often the stated reasoning steps actually support the final end task predictions. In this work, we present ROSCOE, a suite of interpretable, unsupervised automatic scores that improve and extend previous text generation evaluation metrics. To evaluate ROSCOE against baseline metrics, we design a typology of reasoning errors and collect synthetic and human evaluation scores on commonly used reasoning datasets. In contrast with existing metrics, ROSCOE can measure semantic consistency, logicality, informativeness, fluency, and factuality - among other traits - by leveraging properties of step-by-step rationales. We empirically verify the strength of our metrics on five human annotated and six programmatically perturbed diagnostics datasets - covering a diverse set of tasks that require reasoning skills and show that ROSCOE can consistently outperform baseline metrics."]], "0_corpus": "arxiv", "1_conv_id": "1a87d93b61534b849c360a881d6e4c96", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "Metrics for evaluating step-by-step reasoning in language models", "1_output": [["Metrics for evaluating step-by-step reasoning in language models", "Title: ROSCOE: A Suite of Metrics for Scoring Step-by-Step Reasoning\n\nAbstract: Large language models show improved downstream task performance when prompted to generate step-by-step reasoning to justify their final answers. These reasoning steps greatly improve model interpretability and verification, but objectively studying their correctness (independent of the final answer) is difficult without reliable methods for automatic evaluation. We simply do not know how often the stated reasoning steps actually support the final end task predictions. In this work, we present ROSCOE, a suite of interpretable, unsupervised automatic scores that improve and extend previous text generation evaluation metrics. To evaluate ROSCOE against baseline metrics, we design a typology of reasoning errors and collect synthetic and human evaluation scores on commonly used reasoning datasets. In contrast with existing metrics, ROSCOE can measure semantic consistency, logicality, informativeness, fluency, and factuality - among other traits - by leveraging properties of step-by-step rationales. We empirically verify the strength of our metrics on five human annotated and six programmatically perturbed diagnostics datasets - covering a diverse set of tasks that require reasoning skills and show that ROSCOE can consistently outperform baseline metrics."]], "1_corpus": "arxiv"}
+{"tstamp": 1722227122.621, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "118d687cc49c4f9d9169612aa7a8957a", "0_model_name": "mixedbread-ai/mxbai-embed-large-v1", "0_prompt": "Paper focusing on evaluating the effectiveness of large language models in terms of instruction adherence  ", "0_output": [["Paper focusing on evaluating the effectiveness of large language models in terms of instruction adherence  ", "Title: Evaluating Large Language Models at Evaluating Instruction Following\n\nAbstract: As research in large language models (LLMs) continues to accelerate, LLM-based evaluation has emerged as a scalable and cost-effective alternative to human evaluations for comparing the ever increasing list of models. This paper investigates the efficacy of these ``LLM evaluators'', particularly in using them to assess instruction following, a metric that gauges how closely generated text adheres to the given instruction. We introduce a challenging meta-evaluation benchmark, LLMBar, designed to test the ability of an LLM evaluator in discerning instruction-following outputs. The authors manually curated 419 pairs of outputs, one adhering to instructions while the other diverging, yet may possess deceptive qualities that mislead an LLM evaluator, e.g., a more engaging tone. Contrary to existing meta-evaluation, we discover that different evaluators (i.e., combinations of LLMs and prompts) exhibit distinct performance on LLMBar and even the highest-scoring ones have substantial room for improvement. We also present a novel suite of prompting strategies that further close the gap between LLM and human evaluators. With LLMBar, we hope to offer more insight into LLM evaluators and foster future research in developing better instruction-following models."]], "0_corpus": "arxiv", "1_conv_id": "0d726137660541c283b405566e8e9a21", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "Paper focusing on evaluating the effectiveness of large language models in terms of instruction adherence  ", "1_output": [["Paper focusing on evaluating the effectiveness of large language models in terms of instruction adherence  ", "Title: Evaluating Large Language Models at Evaluating Instruction Following\n\nAbstract: As research in large language models (LLMs) continues to accelerate, LLM-based evaluation has emerged as a scalable and cost-effective alternative to human evaluations for comparing the ever increasing list of models. This paper investigates the efficacy of these ``LLM evaluators'', particularly in using them to assess instruction following, a metric that gauges how closely generated text adheres to the given instruction. We introduce a challenging meta-evaluation benchmark, LLMBar, designed to test the ability of an LLM evaluator in discerning instruction-following outputs. The authors manually curated 419 pairs of outputs, one adhering to instructions while the other diverging, yet may possess deceptive qualities that mislead an LLM evaluator, e.g., a more engaging tone. Contrary to existing meta-evaluation, we discover that different evaluators (i.e., combinations of LLMs and prompts) exhibit distinct performance on LLMBar and even the highest-scoring ones have substantial room for improvement. We also present a novel suite of prompting strategies that further close the gap between LLM and human evaluators. With LLMBar, we hope to offer more insight into LLM evaluators and foster future research in developing better instruction-following models."]], "1_corpus": "arxiv"}
+{"tstamp": 1722227128.3311, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "457524b04a99465e8c61334e8a23df34", "0_model_name": "voyage-multilingual-2", "0_prompt": "Tree-based Transformer model for code generation and long dependency handling", "0_output": [["Tree-based Transformer model for code generation and long dependency handling", "Title: TreeGen: A Tree-Based Transformer Architecture for Code Generation\n\nAbstract: A code generation system generates programming language code based on an input natural language description. State-of-the-art approaches rely on neural networks for code generation. However, these code generators suffer from two problems. One is the long dependency problem, where a code element often depends on another far-away code element. A variable reference, for example, depends on its definition, which may appear quite a few lines before. The other problem is structure modeling, as programs contain rich structural information. In this paper, we propose a novel tree-based neural architecture, TreeGen, for code generation. TreeGen uses the attention mechanism of Transformers to alleviate the long-dependency problem, and introduces a novel AST reader (encoder) to incorporate grammar rules and AST structures into the network. We evaluated TreeGen on a Python benchmark, HearthStone, and two semantic parsing benchmarks, ATIS and GEO. TreeGen outperformed the previous state-of-the-art approach by 4.5 percentage points on HearthStone, and achieved the best accuracy among neural network-based approaches on ATIS (89.1%) and GEO (89.6%). We also conducted an ablation test to better understand each component of our model."]], "0_corpus": "arxiv", "1_conv_id": "beab0578168746e2b70b8a85f759bc3a", "1_model_name": "intfloat/multilingual-e5-large-instruct", "1_prompt": "Tree-based Transformer model for code generation and long dependency handling", "1_output": [["Tree-based Transformer model for code generation and long dependency handling", "Title: TreeGen: A Tree-Based Transformer Architecture for Code Generation\n\nAbstract: A code generation system generates programming language code based on an input natural language description. State-of-the-art approaches rely on neural networks for code generation. However, these code generators suffer from two problems. One is the long dependency problem, where a code element often depends on another far-away code element. A variable reference, for example, depends on its definition, which may appear quite a few lines before. The other problem is structure modeling, as programs contain rich structural information. In this paper, we propose a novel tree-based neural architecture, TreeGen, for code generation. TreeGen uses the attention mechanism of Transformers to alleviate the long-dependency problem, and introduces a novel AST reader (encoder) to incorporate grammar rules and AST structures into the network. We evaluated TreeGen on a Python benchmark, HearthStone, and two semantic parsing benchmarks, ATIS and GEO. TreeGen outperformed the previous state-of-the-art approach by 4.5 percentage points on HearthStone, and achieved the best accuracy among neural network-based approaches on ATIS (89.1%) and GEO (89.6%). We also conducted an ablation test to better understand each component of our model."]], "1_corpus": "arxiv"}
+{"tstamp": 1722227147.2195, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "cfdfbee509bf444ebe1455fb9cd8068a", "0_model_name": "BAAI/bge-large-en-v1.5", "0_prompt": "Study showing improvements in spatio-temporal video grounding with transformer-based methods", "0_output": [["Study showing improvements in spatio-temporal video grounding with transformer-based methods", "Title: Human-centric Spatio-Temporal Video Grounding With Visual Transformers\n\nAbstract: In this work, we introduce a novel task - Humancentric Spatio-Temporal Video Grounding (HC-STVG). Unlike the existing referring expression tasks in images or videos, by focusing on humans, HC-STVG aims to localize a spatiotemporal tube of the target person from an untrimmed video based on a given textural description. This task is useful, especially for healthcare and security-related applications, where the surveillance videos can be extremely long but only a specific person during a specific period of time is concerned. HC-STVG is a video grounding task that requires both spatial (where) and temporal (when) localization. Unfortunately, the existing grounding methods cannot handle this task well. We tackle this task by proposing an effective baseline method named Spatio-Temporal Grounding with Visual Transformers (STGVT), which utilizes Visual Transformers to extract cross-modal representations for video-sentence matching and temporal localization. To facilitate this task, we also contribute an HC-STVG dataset consisting of 5,660 video-sentence pairs on complex multi-person scenes. Specifically, each video lasts for 20 seconds, pairing with a natural query sentence with an average of 17.25 words. Extensive experiments are conducted on this dataset, demonstrating the newly-proposed method outperforms the existing baseline methods."]], "0_corpus": "arxiv", "1_conv_id": "dcfe4354507d43b6a4ccdfce0c60a4a5", "1_model_name": "text-embedding-3-large", "1_prompt": "Study showing improvements in spatio-temporal video grounding with transformer-based methods", "1_output": [["Study showing improvements in spatio-temporal video grounding with transformer-based methods", "Title: Grounding Spatio-Temporal Language with Transformers\n\nAbstract: Language is an interface to the outside world. In order for embodied agents to use it, language must be grounded in other, sensorimotor modalities. While there is an extended literature studying how machines can learn grounded language, the topic of how to learn spatio-temporal linguistic concepts is still largely uncharted. To make progress in this direction, we here introduce a novel spatio-temporal language grounding task where the goal is to learn the meaning of spatio-temporal descriptions of behavioral traces of an embodied agent. This is achieved by training a truth function that predicts if a description matches a given history of observations. The descriptions involve time-extended predicates in past and present tense as well as spatio-temporal references to objects in the scene. To study the role of architectural biases in this task, we train several models including multimodal Transformer architectures; the latter implement different attention computations between words and objects across space and time. We test models on two classes of generalization: 1) generalization to randomly held-out sentences; 2) generalization to grammar primitives. We observe that maintaining object identity in the attention computation of our Transformers is instrumental to achieving good performance on generalization overall, and that summarizing object traces in a single token has little influence on performance. We then discuss how this opens new perspectives for language-guided autonomous embodied agents. We also release our code under open-source license as well as pretrained models and datasets to encourage the wider community to build upon and extend our work in the future."]], "1_corpus": "arxiv"}
+{"tstamp": 1722227169.0685, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "2f3d197a65dc467ca5bfd7baa9795464", "0_model_name": "BAAI/bge-large-en-v1.5", "0_prompt": "A model improving question generation and answering with Transformers in semi-supervised setup.", "0_output": [["A model improving question generation and answering with Transformers in semi-supervised setup.", "Title: Machine Comprehension by Text-to-Text Neural Question Generation\n\nAbstract: We propose a recurrent neural model that generates natural-language questions from documents, conditioned on answers. We show how to train the model using a combination of supervised and reinforcement learning. After teacher forcing for standard maximum likelihood training, we fine-tune the model using policy gradient techniques to maximize several rewards that measure question quality. Most notably, one of these rewards is the performance of a question-answering system. We motivate question generation as a means to improve the performance of question answering systems. Our model is trained and evaluated on the recent question-answering dataset SQuAD."]], "0_corpus": "arxiv", "1_conv_id": "6fe550931a3f4d108db6ae848b48f728", "1_model_name": "jinaai/jina-embeddings-v2-base-en", "1_prompt": "A model improving question generation and answering with Transformers in semi-supervised setup.", "1_output": [["A model improving question generation and answering with Transformers in semi-supervised setup.", "Title: Hexagons and Interfaces in a Vibrated Granular Layer\n\nAbstract: The order parameter model based on parametric Ginzburg-Landau equation is used to describe high acceleration patterns in vibrated layer of granular material. At large amplitude of driving both hexagons and interfaces emerge. Transverse instability leading to formation of ``decorated'' interfaces and labyrinthine patterns, is found. Additional sub-harmonic forcing leads to controlled interface motion."]], "1_corpus": "arxiv"}
+{"tstamp": 1722227184.2501, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "df087591632840268d7cda35b4eee3d6", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": "A model replacing RNN/CNN with self-attention for better HAR results  ", "0_output": [["A model replacing RNN/CNN with self-attention for better HAR results  ", "Title: Partial Representations and Amenable Fell Bundles over Free Groups\n\nAbstract: We show that a Fell bundle B = {B_t}_{t \\in F}, over an arbitrary free group F, is amenable, whenever it is orthogonal (in the sense that B_x^* B_y = 0, if x and y are distinct generators of F) and semi-saturated (in the sense that B_{ts} coincides with the closed linear span of B_t B_s, when the multiplication ``ts'' involves no cancelation)."]], "0_corpus": "arxiv", "1_conv_id": "39bbefcfdcb54d1fbfae7a726a6a9260", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": "A model replacing RNN/CNN with self-attention for better HAR results  ", "1_output": [["A model replacing RNN/CNN with self-attention for better HAR results  ", "Title: Electromagnetic transitions with effective operators\n\nAbstract: In the no-core shell model formalism we compute effective one- and two-body operators, using the Lee-Suzuki procedure within the two-body cluster approximation. We evaluate the validity of the latter through calculations in reduced model spaces. In particular, we test the results for the two-body system and find that indeed the effective operators in the reduced space reproduce the expectation values or transition strengths computed in the full space. On the other hand, the renormalization for operators in the case of 6Li is very weak, suggesting the need for higher-body clusters in computing the effective interaction."]], "1_corpus": "arxiv"}
+{"tstamp": 1722227262.9897, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "06e0a8ed452a4140b64f3fc3ef65afc7", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "can you travel to kenya with an expired passport?", "0_output": [["can you travel to kenya with an expired passport?", "Gaming Stackexchange\n\nQ: Can I get a document back after I have returned it? After approving a migrant worker's passport and returning it to them, I realized that it was expired. I still have their work ticket, which appears valid. Is there any way to deny them entry to the country at this point?\n\nA: Nope.\nWell, you just made a big mistake: You approved before checking everything. The game is very harsh in this regard and does not allow you to roll back your decision, once you have given back the document.\nThis is part of the message - Think before you act, even if it takes valuable time ... Or just push your luck.\nAlso: You don't get fined immediately for a simple error - you get two chances every day to make mistakes (intentional or otherwise), and then the fines start kicking in.\n"]], "0_corpus": "stackexchange", "1_conv_id": "18595514e0f1447ea4ec341445ee2f8d", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "can you travel to kenya with an expired passport?", "1_output": [["can you travel to kenya with an expired passport?", "Gaming Stackexchange\n\nQ: Can I get a document back after I have returned it? After approving a migrant worker's passport and returning it to them, I realized that it was expired. I still have their work ticket, which appears valid. Is there any way to deny them entry to the country at this point?\n\nA: Nope.\nWell, you just made a big mistake: You approved before checking everything. The game is very harsh in this regard and does not allow you to roll back your decision, once you have given back the document.\nThis is part of the message - Think before you act, even if it takes valuable time ... Or just push your luck.\nAlso: You don't get fined immediately for a simple error - you get two chances every day to make mistakes (intentional or otherwise), and then the fines start kicking in.\n"]], "1_corpus": "stackexchange"}
+{"tstamp": 1722227306.3763, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "5dea31ed6e6640a48d84eccc072395b9", "0_model_name": "text-embedding-3-large", "0_prompt": "Paper on GPt models for embedding", "0_output": [["Paper on GPt models for embedding", "Title: Gaussian Process Topic Models\n\nAbstract: We introduce Gaussian Process Topic Models (GPTMs), a new family of topic models which can leverage a kernel among documents while extracting correlated topics. GPTMs can be considered a systematic generalization of the Correlated Topic Models (CTMs) using ideas from Gaussian Process (GP) based embedding. Since GPTMs work with both a topic covariance matrix and a document kernel matrix, learning GPTMs involves a novel component-solving a suitable Sylvester equation capturing both topic and document dependencies. The efficacy of GPTMs is demonstrated with experiments evaluating the quality of both topic modeling and embedding."]], "0_corpus": "arxiv", "1_conv_id": "64b61d37f88c4e7f90958d2817eca881", "1_model_name": "intfloat/multilingual-e5-large-instruct", "1_prompt": "Paper on GPt models for embedding", "1_output": [["Paper on GPt models for embedding", "Title: Nimble GNN Embedding with Tensor-Train Decomposition\n\nAbstract: This paper describes a new method for representing embedding tables of graph neural networks (GNNs) more compactly via tensor-train (TT) decomposition. We consider the scenario where (a) the graph data that lack node features, thereby requiring the learning of embeddings during training; and (b) we wish to exploit GPU platforms, where smaller tables are needed to reduce host-to-GPU communication even for large-memory GPUs. The use of TT enables a compact parameterization of the embedding, rendering it small enough to fit entirely on modern GPUs even for massive graphs. When combined with judicious schemes for initialization and hierarchical graph partitioning, this approach can reduce the size of node embedding vectors by 1,659 times to 81,362 times on large publicly available benchmark datasets, achieving comparable or better accuracy and significant speedups on multi-GPU systems. In some cases, our model without explicit node features on input can even match the accuracy of models that use node features."]], "1_corpus": "arxiv"}

data/retrieval_individual-f0f24c65-6f65-4435-a1e5-20bb12a2b295.jsonl

@@ -125,3 +125,39 @@
 {"tstamp": 1722227001.4038, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722227001.3034, "finish": 1722227001.4038, "ip": "", "conv_id": "339520347d484e1c8068e44e4e4e7452", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "Achieving high accuracy in English-French plagiarism detection using word embeddings", "output": [["Achieving high accuracy in English-French plagiarism detection using word embeddings", "Title: Studies of Plasma Detachment Using a One Dimensional Model for Divertor Operation\n\nAbstract: To characterize the conditions required to reach advanced divertor regimes, a one-dimensional computational model has been developed based on a coordinate transformation to incorporate two-dimensional effects. This model includes transport of ions, two species each of atoms and molecules, momentum, and ion and electron energy both within and across the flux surfaces. Impurity radiation is calculated using a coronal equilibrium model which includes the effects of charge-exchange recombination. Numerical results indicate that impurity radiation acts to facilitate plasma detachment and enhances the power lost from the divertor channel in escaping neutral atoms by cooling the electrons and suppressing ionization. As divertor particle densities increase, cold and thermal molecules become increasingly important in cooling the plasma, with molecular densities dominating electron and atomic densities under some conditions."]], "corpus": "arxiv"}
 {"tstamp": 1722227013.155, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1722227012.9814, "finish": 1722227013.155, "ip": "", "conv_id": "de8d07d9dc434154b214bde3478b3319", "model_name": "BM25", "prompt": "A paper presenting a Transformer model for vision-and-language tasks that does not rely on object detection or ResNet.", "output": [["A paper presenting a Transformer model for vision-and-language tasks that does not rely on object detection or ResNet.", "Title: ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision\n\nAbstract: Vision-and-Language Pre-training (VLP) has improved performance on various joint vision-and-language downstream tasks. Current approaches to VLP heavily rely on image feature extraction processes, most of which involve region supervision (e.g., object detection) and the convolutional architecture (e.g., ResNet). Although disregarded in the literature, we find it problematic in terms of both (1) efficiency/speed, that simply extracting input features requires much more computation than the multimodal interaction steps; and (2) expressive power, as it is upper bounded to the expressive power of the visual embedder and its predefined visual vocabulary. In this paper, we present a minimal VLP model, Vision-and-Language Transformer (ViLT), monolithic in the sense that the processing of visual inputs is drastically simplified to just the same convolution-free manner that we process textual inputs. We show that ViLT is up to tens of times faster than previous VLP models, yet with competitive or better downstream task performance. Our code and pre-trained weights are available at https://github.com/dandelin/vilt."]], "corpus": "arxiv"}
 {"tstamp": 1722227013.155, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1722227012.9814, "finish": 1722227013.155, "ip": "", "conv_id": "21bf134438c34376b884b388bf7b8c19", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": "A paper presenting a Transformer model for vision-and-language tasks that does not rely on object detection or ResNet.", "output": [["A paper presenting a Transformer model for vision-and-language tasks that does not rely on object detection or ResNet.", "Title: ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision\n\nAbstract: Vision-and-Language Pre-training (VLP) has improved performance on various joint vision-and-language downstream tasks. Current approaches to VLP heavily rely on image feature extraction processes, most of which involve region supervision (e.g., object detection) and the convolutional architecture (e.g., ResNet). Although disregarded in the literature, we find it problematic in terms of both (1) efficiency/speed, that simply extracting input features requires much more computation than the multimodal interaction steps; and (2) expressive power, as it is upper bounded to the expressive power of the visual embedder and its predefined visual vocabulary. In this paper, we present a minimal VLP model, Vision-and-Language Transformer (ViLT), monolithic in the sense that the processing of visual inputs is drastically simplified to just the same convolution-free manner that we process textual inputs. We show that ViLT is up to tens of times faster than previous VLP models, yet with competitive or better downstream task performance. Our code and pre-trained weights are available at https://github.com/dandelin/vilt."]], "corpus": "arxiv"}
+{"tstamp": 1722227020.2398, "task_type": "retrieval", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722227019.6047, "finish": 1722227020.2398, "ip": "", "conv_id": "13b46854dae44ccfa5b050966e192eba", "model_name": "text-embedding-004", "prompt": "How do explanations in context influence the performance of language models?", "output": [["How do explanations in context influence the performance of language models?", "Title: Can language models learn from explanations in context?\n\nAbstract: Language Models (LMs) can perform new tasks by adapting to a few in-context examples. For humans, explanations that connect examples to task principles can improve learning. We therefore investigate whether explanations of few-shot examples can help LMs. We annotate questions from 40 challenging tasks with answer explanations, and various matched control explanations. We evaluate how different types of explanations, instructions, and controls affect zero- and few-shot performance. We analyze these results using statistical multilevel modeling techniques that account for the nested dependencies among conditions, tasks, prompts, and models. We find that explanations can improve performance -- even without tuning. Furthermore, explanations hand-tuned for performance on a small validation set offer substantially larger benefits, and building a prompt by selecting examples and explanations together substantially improves performance over selecting examples alone. Finally, even untuned explanations outperform carefully matched controls, suggesting that the benefits are due to the link between an example and its explanation, rather than lower-level features. However, only large models benefit. In summary, explanations can support the in-context learning of large LMs on challenging tasks."]], "corpus": "arxiv"}
+{"tstamp": 1722227020.2398, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722227019.6047, "finish": 1722227020.2398, "ip": "", "conv_id": "f0f4c01bfc3f47babe5c73dfa7135da2", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "How do explanations in context influence the performance of language models?", "output": [["How do explanations in context influence the performance of language models?", "Title: Can language models learn from explanations in context?\n\nAbstract: Language Models (LMs) can perform new tasks by adapting to a few in-context examples. For humans, explanations that connect examples to task principles can improve learning. We therefore investigate whether explanations of few-shot examples can help LMs. We annotate questions from 40 challenging tasks with answer explanations, and various matched control explanations. We evaluate how different types of explanations, instructions, and controls affect zero- and few-shot performance. We analyze these results using statistical multilevel modeling techniques that account for the nested dependencies among conditions, tasks, prompts, and models. We find that explanations can improve performance -- even without tuning. Furthermore, explanations hand-tuned for performance on a small validation set offer substantially larger benefits, and building a prompt by selecting examples and explanations together substantially improves performance over selecting examples alone. Finally, even untuned explanations outperform carefully matched controls, suggesting that the benefits are due to the link between an example and its explanation, rather than lower-level features. However, only large models benefit. In summary, explanations can support the in-context learning of large LMs on challenging tasks."]], "corpus": "arxiv"}
+{"tstamp": 1722227026.6265, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1722227026.1188, "finish": 1722227026.6265, "ip": "", "conv_id": "ce9a309e5ad24ba38deee7ff08db73eb", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": "Study introducing a BERT-based framework achieving superior performance on scalable DST benchmarks like Sim-M and Sim-R.", "output": [["Study introducing a BERT-based framework achieving superior performance on scalable DST benchmarks like Sim-M and Sim-R.", "Title: BERT-DST: Scalable End-to-End Dialogue State Tracking with Bidirectional Encoder Representations from Transformer\n\nAbstract: An important yet rarely tackled problem in dialogue state tracking (DST) is scalability for dynamic ontology (e.g., movie, restaurant) and unseen slot values. We focus on a specific condition, where the ontology is unknown to the state tracker, but the target slot value (except for none and dontcare), possibly unseen during training, can be found as word segment in the dialogue context. Prior approaches often rely on candidate generation from n-gram enumeration or slot tagger outputs, which can be inefficient or suffer from error propagation. We propose BERT-DST, an end-to-end dialogue state tracker which directly extracts slot values from the dialogue context. We use BERT as dialogue context encoder whose contextualized language representations are suitable for scalable DST to identify slot values from their semantic context. Furthermore, we employ encoder parameter sharing across all slots with two advantages: (1) Number of parameters does not grow linearly with the ontology. (2) Language representation knowledge can be transferred among slots. Empirical evaluation shows BERT-DST with cross-slot parameter sharing outperforms prior work on the benchmark scalable DST datasets Sim-M and Sim-R, and achieves competitive performance on the standard DSTC2 and WOZ 2.0 datasets."]], "corpus": "arxiv"}
+{"tstamp": 1722227026.6265, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1722227026.1188, "finish": 1722227026.6265, "ip": "", "conv_id": "fa33616b16934385bfd92cd0a0c413cf", "model_name": "text-embedding-3-large", "prompt": "Study introducing a BERT-based framework achieving superior performance on scalable DST benchmarks like Sim-M and Sim-R.", "output": [["Study introducing a BERT-based framework achieving superior performance on scalable DST benchmarks like Sim-M and Sim-R.", "Title: BERT-DST: Scalable End-to-End Dialogue State Tracking with Bidirectional Encoder Representations from Transformer\n\nAbstract: An important yet rarely tackled problem in dialogue state tracking (DST) is scalability for dynamic ontology (e.g., movie, restaurant) and unseen slot values. We focus on a specific condition, where the ontology is unknown to the state tracker, but the target slot value (except for none and dontcare), possibly unseen during training, can be found as word segment in the dialogue context. Prior approaches often rely on candidate generation from n-gram enumeration or slot tagger outputs, which can be inefficient or suffer from error propagation. We propose BERT-DST, an end-to-end dialogue state tracker which directly extracts slot values from the dialogue context. We use BERT as dialogue context encoder whose contextualized language representations are suitable for scalable DST to identify slot values from their semantic context. Furthermore, we employ encoder parameter sharing across all slots with two advantages: (1) Number of parameters does not grow linearly with the ontology. (2) Language representation knowledge can be transferred among slots. Empirical evaluation shows BERT-DST with cross-slot parameter sharing outperforms prior work on the benchmark scalable DST datasets Sim-M and Sim-R, and achieves competitive performance on the standard DSTC2 and WOZ 2.0 datasets."]], "corpus": "arxiv"}
+{"tstamp": 1722227036.8182, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722227036.6406, "finish": 1722227036.8182, "ip": "", "conv_id": "d4f40214f39349929660960ef995c744", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "A study showing the drawbacks of using standard MLE training and offering a new training procedure for better test performance in language models.", "output": [["A study showing the drawbacks of using standard MLE training and offering a new training procedure for better test performance in language models.", "Title: Recurrent Neural Language Models as Probabilistic Finite-state Automata\n\nAbstract: Studying language models (LMs) in terms of well-understood formalisms allows us to precisely characterize their abilities and limitations. Previous work has investigated the representational capacity of recurrent neural network (RNN) LMs in terms of their capacity to recognize unweighted formal languages. However, LMs do not describe unweighted formal languages -- rather, they define \\emph{probability distributions} over strings. In this work, we study what classes of such probability distributions RNN LMs can represent, which allows us to make more direct statements about their capabilities. We show that simple RNNs are equivalent to a subclass of probabilistic finite-state automata, and can thus model a strict subset of probability distributions expressible by finite-state models. Furthermore, we study the space complexity of representing finite-state LMs with RNNs. We show that, to represent an arbitrary deterministic finite-state LM with $N$ states over an alphabet $\\alphabet$, an RNN requires $\\Omega\\left(N |\\Sigma|\\right)$ neurons. These results present a first step towards characterizing the classes of distributions RNN LMs can represent and thus help us understand their capabilities and limitations."]], "corpus": "arxiv"}
+{"tstamp": 1722227036.8182, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722227036.6406, "finish": 1722227036.8182, "ip": "", "conv_id": "d39dbe79ef5d443683896e332508c895", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "A study showing the drawbacks of using standard MLE training and offering a new training procedure for better test performance in language models.", "output": [["A study showing the drawbacks of using standard MLE training and offering a new training procedure for better test performance in language models.", "Title: Neural Architecture Search as Sparse Supernet\n\nAbstract: This paper aims at enlarging the problem of Neural Architecture Search (NAS) from Single-Path and Multi-Path Search to automated Mixed-Path Search. In particular, we model the NAS problem as a sparse supernet using a new continuous architecture representation with a mixture of sparsity constraints. The sparse supernet enables us to automatically achieve sparsely-mixed paths upon a compact set of nodes. To optimize the proposed sparse supernet, we exploit a hierarchical accelerated proximal gradient algorithm within a bi-level optimization framework. Extensive experiments on Convolutional Neural Network and Recurrent Neural Network search demonstrate that the proposed method is capable of searching for compact, general and powerful neural architectures."]], "corpus": "arxiv"}
+{"tstamp": 1722227070.1836, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722227070.0115, "finish": 1722227070.1836, "ip": "", "conv_id": "ec5eb017dc4d4d9fa6d04d114fcc2e00", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "Propaganda-as-a-service via training-time attacks on language models for biased text generation  ", "output": [["Propaganda-as-a-service via training-time attacks on language models for biased text generation  ", "Title: Spinning Language Models: Risks of Propaganda-As-A-Service and Countermeasures\n\nAbstract: We investigate a new threat to neural sequence-to-sequence (seq2seq) models: training-time attacks that cause models to \"spin\" their outputs so as to support an adversary-chosen sentiment or point of view -- but only when the input contains adversary-chosen trigger words. For example, a spinned summarization model outputs positive summaries of any text that mentions the name of some individual or organization. Model spinning introduces a \"meta-backdoor\" into a model. Whereas conventional backdoors cause models to produce incorrect outputs on inputs with the trigger, outputs of spinned models preserve context and maintain standard accuracy metrics, yet also satisfy a meta-task chosen by the adversary. Model spinning enables propaganda-as-a-service, where propaganda is defined as biased speech. An adversary can create customized language models that produce desired spins for chosen triggers, then deploy these models to generate disinformation (a platform attack), or else inject them into ML training pipelines (a supply-chain attack), transferring malicious functionality to downstream models trained by victims. To demonstrate the feasibility of model spinning, we develop a new backdooring technique. It stacks an adversarial meta-task onto a seq2seq model, backpropagates the desired meta-task output to points in the word-embedding space we call \"pseudo-words,\" and uses pseudo-words to shift the entire output distribution of the seq2seq model. We evaluate this attack on language generation, summarization, and translation models with different triggers and meta-tasks such as sentiment, toxicity, and entailment. Spinned models largely maintain their accuracy metrics (ROUGE and BLEU) while shifting their outputs to satisfy the adversary's meta-task. We also show that, in the case of a supply-chain attack, the spin functionality transfers to downstream models."]], "corpus": "arxiv"}
+{"tstamp": 1722227070.1836, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722227070.0115, "finish": 1722227070.1836, "ip": "", "conv_id": "009892afcd5f438aa105fea295c61e62", "model_name": "GritLM/GritLM-7B", "prompt": "Propaganda-as-a-service via training-time attacks on language models for biased text generation  ", "output": [["Propaganda-as-a-service via training-time attacks on language models for biased text generation  ", "Title: Spinning Language Models: Risks of Propaganda-As-A-Service and Countermeasures\n\nAbstract: We investigate a new threat to neural sequence-to-sequence (seq2seq) models: training-time attacks that cause models to \"spin\" their outputs so as to support an adversary-chosen sentiment or point of view -- but only when the input contains adversary-chosen trigger words. For example, a spinned summarization model outputs positive summaries of any text that mentions the name of some individual or organization. Model spinning introduces a \"meta-backdoor\" into a model. Whereas conventional backdoors cause models to produce incorrect outputs on inputs with the trigger, outputs of spinned models preserve context and maintain standard accuracy metrics, yet also satisfy a meta-task chosen by the adversary. Model spinning enables propaganda-as-a-service, where propaganda is defined as biased speech. An adversary can create customized language models that produce desired spins for chosen triggers, then deploy these models to generate disinformation (a platform attack), or else inject them into ML training pipelines (a supply-chain attack), transferring malicious functionality to downstream models trained by victims. To demonstrate the feasibility of model spinning, we develop a new backdooring technique. It stacks an adversarial meta-task onto a seq2seq model, backpropagates the desired meta-task output to points in the word-embedding space we call \"pseudo-words,\" and uses pseudo-words to shift the entire output distribution of the seq2seq model. We evaluate this attack on language generation, summarization, and translation models with different triggers and meta-tasks such as sentiment, toxicity, and entailment. Spinned models largely maintain their accuracy metrics (ROUGE and BLEU) while shifting their outputs to satisfy the adversary's meta-task. We also show that, in the case of a supply-chain attack, the spin functionality transfers to downstream models."]], "corpus": "arxiv"}
+{"tstamp": 1722227078.9067, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722227078.778, "finish": 1722227078.9067, "ip": "", "conv_id": "a76136d3818e49c29e4baa8391ebbab2", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "Research on the effectiveness of chain-of-thought prompting to solve difficult language model tasks", "output": [["Research on the effectiveness of chain-of-thought prompting to solve difficult language model tasks", "Title: System-Level Genetic Codes: An Explanation for Biological Complexity\n\nAbstract: Complex systems with tightly coadapted parts frequently appear in living systems and are difficult to account for through Darwinian evolution, that is random variation and natural selection, if the constituent parts are independently coded in the genetic code. If the parts are independently coded, multiple simultaneous mutations appear necessary to create or modify these systems. It is generally believed that most proteins are independently coded. The textbook rule is one gene for one enzyme. Thus, biochemical systems with tightly coadapted parts such as the blood clotting cascade pose a difficulty for Darwinian evolution. This problem can be overcome if the current understanding of the genetic code is incomplete and a system-level genetic code in which seemingly independent proteins are encoded in an interdependent, highly correlated manner exists. The methods by which human beings design and fabricate complex systems of tightly coadapted parts are explored for insights into the requirements for a system-level genetic code. Detailed examples of system-level codes for networks of matching parts are presented. The implications of identifying and deciphering the system-level genetic code if it exists for the prevention, treatment, and cure of heart disease, cancer, immune disorders, and for rational drug design are discussed."]], "corpus": "arxiv"}
+{"tstamp": 1722227078.9067, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1722227078.778, "finish": 1722227078.9067, "ip": "", "conv_id": "891e5ee1250b4d1fac7f62baa9315f5e", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": "Research on the effectiveness of chain-of-thought prompting to solve difficult language model tasks", "output": [["Research on the effectiveness of chain-of-thought prompting to solve difficult language model tasks", "Title: ChainLM: Empowering Large Language Models with Improved Chain-of-Thought Prompting\n\nAbstract: Chain-of-Thought (CoT) prompting can enhance the reasoning capabilities of large language models (LLMs), establishing itself as a primary approach to solving complex reasoning tasks. Existing CoT synthesis approaches usually focus on simpler reasoning tasks and thus result in low-quality and inconsistent CoT prompts. In response to this challenge, we present an empirical investigation of CoT prompting and introduce CoTGenius, a novel framework designed for the automatic generation of superior CoT prompts. CoTGenius is developed based on three major evolution strategies, i.e., complicate, diversify, and specify-alongside two filtering mechanisms: evolutionary success judgement and correctness verification. We further employ CoTGenius to create an extensive CoT dataset, and subsequently fine-tune the Llama 2-Chat 7B and 13B models on this dataset. We call the resulting model ChainLM. To deal with the cumulative error issue in reasoning steps, we propose a step-level debating method, wherein multiple debaters discuss each reasoning step to arrive at the correct answer. Extensive experiments demonstrate that our ChainLM models exhibit enhanced proficiency in addressing a spectrum of complex reasoning problems compared to existing models. In addition, we conduct an in-depth analysis of the impact of data categories within CoTGenius on the model performance. We release our dataset and code at https://github.com/RUCAIBox/ChainLM."]], "corpus": "arxiv"}
+{"tstamp": 1722227094.9016, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722227094.7579, "finish": 1722227094.9016, "ip": "", "conv_id": "0b5c946bf2ff4358b48d8d4a1a8e9854", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "Study analyzing how well large language models can assist students in debugging code.", "output": [["Study analyzing how well large language models can assist students in debugging code.", "Title: A characterization of quasipositive Seifert surfaces (Constructions of quasipositive knots and links, III)\n\nAbstract: This article was originally published in Topology 31 (1992). The present hyperTeXed redaction corrects a few typographical errors and updates the references."]], "corpus": "arxiv"}
+{"tstamp": 1722227094.9016, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722227094.7579, "finish": 1722227094.9016, "ip": "", "conv_id": "721c802e8f3b4f46846d3f26b194aee4", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "Study analyzing how well large language models can assist students in debugging code.", "output": [["Study analyzing how well large language models can assist students in debugging code.", "Title: Exploring the Responses of Large Language Models to Beginner Programmers' Help Requests\n\nAbstract: Background and Context: Over the past year, large language models (LLMs) have taken the world by storm. In computing education, like in other walks of life, many opportunities and threats have emerged as a consequence. Objectives: In this article, we explore such opportunities and threats in a specific area: responding to student programmers' help requests. More specifically, we assess how good LLMs are at identifying issues in problematic code that students request help on. Method: We collected a sample of help requests and code from an online programming course. We then prompted two different LLMs (OpenAI Codex and GPT-3.5) to identify and explain the issues in the students' code and assessed the LLM-generated answers both quantitatively and qualitatively. Findings: GPT-3.5 outperforms Codex in most respects. Both LLMs frequently find at least one actual issue in each student program (GPT-3.5 in 90% of the cases). Neither LLM excels at finding all the issues (GPT-3.5 finding them 57% of the time). False positives are common (40% chance for GPT-3.5). The advice that the LLMs provide on the issues is often sensible. The LLMs perform better on issues involving program logic rather than on output formatting. Model solutions are frequently provided even when the LLM is prompted not to. LLM responses to prompts in a non-English language are only slightly worse than responses to English prompts. Implications: Our results continue to highlight the utility of LLMs in programming education. At the same time, the results highlight the unreliability of LLMs: LLMs make some of the same mistakes that students do, perhaps especially when formatting output as required by automated assessment systems. Our study informs teachers interested in using LLMs as well as future efforts to customize LLMs for the needs of programming education."]], "corpus": "arxiv"}
+{"tstamp": 1722227108.0759, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722227107.9384, "finish": 1722227108.0759, "ip": "", "conv_id": "afa919e9ef4546cea9fd3213076acaca", "model_name": "GritLM/GritLM-7B", "prompt": "Metrics for evaluating step-by-step reasoning in language models", "output": [["Metrics for evaluating step-by-step reasoning in language models", "Title: ROSCOE: A Suite of Metrics for Scoring Step-by-Step Reasoning\n\nAbstract: Large language models show improved downstream task performance when prompted to generate step-by-step reasoning to justify their final answers. These reasoning steps greatly improve model interpretability and verification, but objectively studying their correctness (independent of the final answer) is difficult without reliable methods for automatic evaluation. We simply do not know how often the stated reasoning steps actually support the final end task predictions. In this work, we present ROSCOE, a suite of interpretable, unsupervised automatic scores that improve and extend previous text generation evaluation metrics. To evaluate ROSCOE against baseline metrics, we design a typology of reasoning errors and collect synthetic and human evaluation scores on commonly used reasoning datasets. In contrast with existing metrics, ROSCOE can measure semantic consistency, logicality, informativeness, fluency, and factuality - among other traits - by leveraging properties of step-by-step rationales. We empirically verify the strength of our metrics on five human annotated and six programmatically perturbed diagnostics datasets - covering a diverse set of tasks that require reasoning skills and show that ROSCOE can consistently outperform baseline metrics."]], "corpus": "arxiv"}
+{"tstamp": 1722227108.0759, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722227107.9384, "finish": 1722227108.0759, "ip": "", "conv_id": "1a87d93b61534b849c360a881d6e4c96", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "Metrics for evaluating step-by-step reasoning in language models", "output": [["Metrics for evaluating step-by-step reasoning in language models", "Title: ROSCOE: A Suite of Metrics for Scoring Step-by-Step Reasoning\n\nAbstract: Large language models show improved downstream task performance when prompted to generate step-by-step reasoning to justify their final answers. These reasoning steps greatly improve model interpretability and verification, but objectively studying their correctness (independent of the final answer) is difficult without reliable methods for automatic evaluation. We simply do not know how often the stated reasoning steps actually support the final end task predictions. In this work, we present ROSCOE, a suite of interpretable, unsupervised automatic scores that improve and extend previous text generation evaluation metrics. To evaluate ROSCOE against baseline metrics, we design a typology of reasoning errors and collect synthetic and human evaluation scores on commonly used reasoning datasets. In contrast with existing metrics, ROSCOE can measure semantic consistency, logicality, informativeness, fluency, and factuality - among other traits - by leveraging properties of step-by-step rationales. We empirically verify the strength of our metrics on five human annotated and six programmatically perturbed diagnostics datasets - covering a diverse set of tasks that require reasoning skills and show that ROSCOE can consistently outperform baseline metrics."]], "corpus": "arxiv"}
+{"tstamp": 1722227117.9752, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722227117.8556, "finish": 1722227117.9752, "ip": "", "conv_id": "118d687cc49c4f9d9169612aa7a8957a", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "Paper focusing on evaluating the effectiveness of large language models in terms of instruction adherence  ", "output": [["Paper focusing on evaluating the effectiveness of large language models in terms of instruction adherence  ", "Title: Evaluating Large Language Models at Evaluating Instruction Following\n\nAbstract: As research in large language models (LLMs) continues to accelerate, LLM-based evaluation has emerged as a scalable and cost-effective alternative to human evaluations for comparing the ever increasing list of models. This paper investigates the efficacy of these ``LLM evaluators'', particularly in using them to assess instruction following, a metric that gauges how closely generated text adheres to the given instruction. We introduce a challenging meta-evaluation benchmark, LLMBar, designed to test the ability of an LLM evaluator in discerning instruction-following outputs. The authors manually curated 419 pairs of outputs, one adhering to instructions while the other diverging, yet may possess deceptive qualities that mislead an LLM evaluator, e.g., a more engaging tone. Contrary to existing meta-evaluation, we discover that different evaluators (i.e., combinations of LLMs and prompts) exhibit distinct performance on LLMBar and even the highest-scoring ones have substantial room for improvement. We also present a novel suite of prompting strategies that further close the gap between LLM and human evaluators. With LLMBar, we hope to offer more insight into LLM evaluators and foster future research in developing better instruction-following models."]], "corpus": "arxiv"}
+{"tstamp": 1722227117.9752, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722227117.8556, "finish": 1722227117.9752, "ip": "", "conv_id": "0d726137660541c283b405566e8e9a21", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "Paper focusing on evaluating the effectiveness of large language models in terms of instruction adherence  ", "output": [["Paper focusing on evaluating the effectiveness of large language models in terms of instruction adherence  ", "Title: Evaluating Large Language Models at Evaluating Instruction Following\n\nAbstract: As research in large language models (LLMs) continues to accelerate, LLM-based evaluation has emerged as a scalable and cost-effective alternative to human evaluations for comparing the ever increasing list of models. This paper investigates the efficacy of these ``LLM evaluators'', particularly in using them to assess instruction following, a metric that gauges how closely generated text adheres to the given instruction. We introduce a challenging meta-evaluation benchmark, LLMBar, designed to test the ability of an LLM evaluator in discerning instruction-following outputs. The authors manually curated 419 pairs of outputs, one adhering to instructions while the other diverging, yet may possess deceptive qualities that mislead an LLM evaluator, e.g., a more engaging tone. Contrary to existing meta-evaluation, we discover that different evaluators (i.e., combinations of LLMs and prompts) exhibit distinct performance on LLMBar and even the highest-scoring ones have substantial room for improvement. We also present a novel suite of prompting strategies that further close the gap between LLM and human evaluators. With LLMBar, we hope to offer more insight into LLM evaluators and foster future research in developing better instruction-following models."]], "corpus": "arxiv"}
+{"tstamp": 1722227126.6958, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1722227126.328, "finish": 1722227126.6958, "ip": "", "conv_id": "457524b04a99465e8c61334e8a23df34", "model_name": "voyage-multilingual-2", "prompt": "Tree-based Transformer model for code generation and long dependency handling", "output": [["Tree-based Transformer model for code generation and long dependency handling", "Title: TreeGen: A Tree-Based Transformer Architecture for Code Generation\n\nAbstract: A code generation system generates programming language code based on an input natural language description. State-of-the-art approaches rely on neural networks for code generation. However, these code generators suffer from two problems. One is the long dependency problem, where a code element often depends on another far-away code element. A variable reference, for example, depends on its definition, which may appear quite a few lines before. The other problem is structure modeling, as programs contain rich structural information. In this paper, we propose a novel tree-based neural architecture, TreeGen, for code generation. TreeGen uses the attention mechanism of Transformers to alleviate the long-dependency problem, and introduces a novel AST reader (encoder) to incorporate grammar rules and AST structures into the network. We evaluated TreeGen on a Python benchmark, HearthStone, and two semantic parsing benchmarks, ATIS and GEO. TreeGen outperformed the previous state-of-the-art approach by 4.5 percentage points on HearthStone, and achieved the best accuracy among neural network-based approaches on ATIS (89.1%) and GEO (89.6%). We also conducted an ablation test to better understand each component of our model."]], "corpus": "arxiv"}
+{"tstamp": 1722227126.6958, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722227126.328, "finish": 1722227126.6958, "ip": "", "conv_id": "beab0578168746e2b70b8a85f759bc3a", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "Tree-based Transformer model for code generation and long dependency handling", "output": [["Tree-based Transformer model for code generation and long dependency handling", "Title: TreeGen: A Tree-Based Transformer Architecture for Code Generation\n\nAbstract: A code generation system generates programming language code based on an input natural language description. State-of-the-art approaches rely on neural networks for code generation. However, these code generators suffer from two problems. One is the long dependency problem, where a code element often depends on another far-away code element. A variable reference, for example, depends on its definition, which may appear quite a few lines before. The other problem is structure modeling, as programs contain rich structural information. In this paper, we propose a novel tree-based neural architecture, TreeGen, for code generation. TreeGen uses the attention mechanism of Transformers to alleviate the long-dependency problem, and introduces a novel AST reader (encoder) to incorporate grammar rules and AST structures into the network. We evaluated TreeGen on a Python benchmark, HearthStone, and two semantic parsing benchmarks, ATIS and GEO. TreeGen outperformed the previous state-of-the-art approach by 4.5 percentage points on HearthStone, and achieved the best accuracy among neural network-based approaches on ATIS (89.1%) and GEO (89.6%). We also conducted an ablation test to better understand each component of our model."]], "corpus": "arxiv"}
+{"tstamp": 1722227132.5989, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722227132.1413, "finish": 1722227132.5989, "ip": "", "conv_id": "cfdfbee509bf444ebe1455fb9cd8068a", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "Study showing improvements in spatio-temporal video grounding with transformer-based methods", "output": [["Study showing improvements in spatio-temporal video grounding with transformer-based methods", "Title: Human-centric Spatio-Temporal Video Grounding With Visual Transformers\n\nAbstract: In this work, we introduce a novel task - Humancentric Spatio-Temporal Video Grounding (HC-STVG). Unlike the existing referring expression tasks in images or videos, by focusing on humans, HC-STVG aims to localize a spatiotemporal tube of the target person from an untrimmed video based on a given textural description. This task is useful, especially for healthcare and security-related applications, where the surveillance videos can be extremely long but only a specific person during a specific period of time is concerned. HC-STVG is a video grounding task that requires both spatial (where) and temporal (when) localization. Unfortunately, the existing grounding methods cannot handle this task well. We tackle this task by proposing an effective baseline method named Spatio-Temporal Grounding with Visual Transformers (STGVT), which utilizes Visual Transformers to extract cross-modal representations for video-sentence matching and temporal localization. To facilitate this task, we also contribute an HC-STVG dataset consisting of 5,660 video-sentence pairs on complex multi-person scenes. Specifically, each video lasts for 20 seconds, pairing with a natural query sentence with an average of 17.25 words. Extensive experiments are conducted on this dataset, demonstrating the newly-proposed method outperforms the existing baseline methods."]], "corpus": "arxiv"}
+{"tstamp": 1722227132.5989, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1722227132.1413, "finish": 1722227132.5989, "ip": "", "conv_id": "dcfe4354507d43b6a4ccdfce0c60a4a5", "model_name": "text-embedding-3-large", "prompt": "Study showing improvements in spatio-temporal video grounding with transformer-based methods", "output": [["Study showing improvements in spatio-temporal video grounding with transformer-based methods", "Title: Grounding Spatio-Temporal Language with Transformers\n\nAbstract: Language is an interface to the outside world. In order for embodied agents to use it, language must be grounded in other, sensorimotor modalities. While there is an extended literature studying how machines can learn grounded language, the topic of how to learn spatio-temporal linguistic concepts is still largely uncharted. To make progress in this direction, we here introduce a novel spatio-temporal language grounding task where the goal is to learn the meaning of spatio-temporal descriptions of behavioral traces of an embodied agent. This is achieved by training a truth function that predicts if a description matches a given history of observations. The descriptions involve time-extended predicates in past and present tense as well as spatio-temporal references to objects in the scene. To study the role of architectural biases in this task, we train several models including multimodal Transformer architectures; the latter implement different attention computations between words and objects across space and time. We test models on two classes of generalization: 1) generalization to randomly held-out sentences; 2) generalization to grammar primitives. We observe that maintaining object identity in the attention computation of our Transformers is instrumental to achieving good performance on generalization overall, and that summarizing object traces in a single token has little influence on performance. We then discuss how this opens new perspectives for language-guided autonomous embodied agents. We also release our code under open-source license as well as pretrained models and datasets to encourage the wider community to build upon and extend our work in the future."]], "corpus": "arxiv"}
+{"tstamp": 1722227160.6028, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722227160.5002, "finish": 1722227160.6028, "ip": "", "conv_id": "2f3d197a65dc467ca5bfd7baa9795464", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "A model improving question generation and answering with Transformers in semi-supervised setup.", "output": [["A model improving question generation and answering with Transformers in semi-supervised setup.", "Title: Machine Comprehension by Text-to-Text Neural Question Generation\n\nAbstract: We propose a recurrent neural model that generates natural-language questions from documents, conditioned on answers. We show how to train the model using a combination of supervised and reinforcement learning. After teacher forcing for standard maximum likelihood training, we fine-tune the model using policy gradient techniques to maximize several rewards that measure question quality. Most notably, one of these rewards is the performance of a question-answering system. We motivate question generation as a means to improve the performance of question answering systems. Our model is trained and evaluated on the recent question-answering dataset SQuAD."]], "corpus": "arxiv"}
+{"tstamp": 1722227160.6028, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722227160.5002, "finish": 1722227160.6028, "ip": "", "conv_id": "6fe550931a3f4d108db6ae848b48f728", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "A model improving question generation and answering with Transformers in semi-supervised setup.", "output": [["A model improving question generation and answering with Transformers in semi-supervised setup.", "Title: Hexagons and Interfaces in a Vibrated Granular Layer\n\nAbstract: The order parameter model based on parametric Ginzburg-Landau equation is used to describe high acceleration patterns in vibrated layer of granular material. At large amplitude of driving both hexagons and interfaces emerge. Transverse instability leading to formation of ``decorated'' interfaces and labyrinthine patterns, is found. Additional sub-harmonic forcing leads to controlled interface motion."]], "corpus": "arxiv"}
+{"tstamp": 1722227173.3841, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722227173.2783, "finish": 1722227173.3841, "ip": "", "conv_id": "df087591632840268d7cda35b4eee3d6", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "A model replacing RNN/CNN with self-attention for better HAR results  ", "output": [["A model replacing RNN/CNN with self-attention for better HAR results  ", "Title: Partial Representations and Amenable Fell Bundles over Free Groups\n\nAbstract: We show that a Fell bundle B = {B_t}_{t \\in F}, over an arbitrary free group F, is amenable, whenever it is orthogonal (in the sense that B_x^* B_y = 0, if x and y are distinct generators of F) and semi-saturated (in the sense that B_{ts} coincides with the closed linear span of B_t B_s, when the multiplication ``ts'' involves no cancelation)."]], "corpus": "arxiv"}
+{"tstamp": 1722227173.3841, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1722227173.2783, "finish": 1722227173.3841, "ip": "", "conv_id": "39bbefcfdcb54d1fbfae7a726a6a9260", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "A model replacing RNN/CNN with self-attention for better HAR results  ", "output": [["A model replacing RNN/CNN with self-attention for better HAR results  ", "Title: Electromagnetic transitions with effective operators\n\nAbstract: In the no-core shell model formalism we compute effective one- and two-body operators, using the Lee-Suzuki procedure within the two-body cluster approximation. We evaluate the validity of the latter through calculations in reduced model spaces. In particular, we test the results for the two-body system and find that indeed the effective operators in the reduced space reproduce the expectation values or transition strengths computed in the full space. On the other hand, the renormalization for operators in the case of 6Li is very weak, suggesting the need for higher-body clusters in computing the effective interaction."]], "corpus": "arxiv"}
+{"tstamp": 1722227198.6748, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722227198.3259, "finish": 1722227198.6748, "ip": "", "conv_id": "4ec2689a12834a6db1bff4000f809ac7", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "A comprehensive study on an autoregressive language model that achieves competitive results without fine-tuning on translation, question-answering, and other tasks.", "output": [["A comprehensive study on an autoregressive language model that achieves competitive results without fine-tuning on translation, question-answering, and other tasks.", "Title: A Comprehensive Performance Study of Large Language Models on Novel AI Accelerators\n\nAbstract: Artificial intelligence (AI) methods have become critical in scientific applications to help accelerate scientific discovery. Large language models (LLMs) are being considered as a promising approach to address some of the challenging problems because of their superior generalization capabilities across domains. The effectiveness of the models and the accuracy of the applications is contingent upon their efficient execution on the underlying hardware infrastructure. Specialized AI accelerator hardware systems have recently become available for accelerating AI applications. However, the comparative performance of these AI accelerators on large language models has not been previously studied. In this paper, we systematically study LLMs on multiple AI accelerators and GPUs and evaluate their performance characteristics for these models. We evaluate these systems with (i) a micro-benchmark using a core transformer block, (ii) a GPT- 2 model, and (iii) an LLM-driven science use case, GenSLM. We present our findings and analyses of the models' performance to better understand the intrinsic capabilities of AI accelerators. Furthermore, our analysis takes into account key factors such as sequence lengths, scaling behavior, sparsity, and sensitivity to gradient accumulation steps."]], "corpus": "arxiv"}
+{"tstamp": 1722227198.6748, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1722227198.3259, "finish": 1722227198.6748, "ip": "", "conv_id": "fc6506362156431bbfcc4838c0170354", "model_name": "voyage-multilingual-2", "prompt": "A comprehensive study on an autoregressive language model that achieves competitive results without fine-tuning on translation, question-answering, and other tasks.", "output": [["A comprehensive study on an autoregressive language model that achieves competitive results without fine-tuning on translation, question-answering, and other tasks.", "Title: What Have We Achieved on Non-autoregressive Translation?\n\nAbstract: Recent advances have made non-autoregressive (NAT) translation comparable to autoregressive methods (AT). However, their evaluation using BLEU has been shown to weakly correlate with human annotations. Limited research compares non-autoregressive translation and autoregressive translation comprehensively, leaving uncertainty about the true proximity of NAT to AT. To address this gap, we systematically evaluate four representative NAT methods across various dimensions, including human evaluation. Our empirical results demonstrate that despite narrowing the performance gap, state-of-the-art NAT still underperforms AT under more reliable evaluation metrics. Furthermore, we discover that explicitly modeling dependencies is crucial for generating natural language and generalizing to out-of-distribution sequences."]], "corpus": "arxiv"}
+{"tstamp": 1722227223.7108, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722227223.0167, "finish": 1722227223.7108, "ip": "", "conv_id": "a94f5283b42849a2a94a8bea42b41dfa", "model_name": "GritLM/GritLM-7B", "prompt": "Seeking a paper on refining model predictions layer-by-layer in transformers with improved techniques.", "output": [["Seeking a paper on refining model predictions layer-by-layer in transformers with improved techniques.", "Title: Eliciting Latent Predictions from Transformers with the Tuned Lens\n\nAbstract: We analyze transformers from the perspective of iterative inference, seeking to understand how model predictions are refined layer by layer. To do so, we train an affine probe for each block in a frozen pretrained model, making it possible to decode every hidden state into a distribution over the vocabulary. Our method, the \\emph{tuned lens}, is a refinement of the earlier ``logit lens'' technique, which yielded useful insights but is often brittle. We test our method on various autoregressive language models with up to 20B parameters, showing it to be more predictive, reliable and unbiased than the logit lens. With causal experiments, we show the tuned lens uses similar features to the model itself. We also find the trajectory of latent predictions can be used to detect malicious inputs with high accuracy. All code needed to reproduce our results can be found at https://github.com/AlignmentResearch/tuned-lens."]], "corpus": "arxiv"}
+{"tstamp": 1722227223.7108, "task_type": "retrieval", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722227223.0167, "finish": 1722227223.7108, "ip": "", "conv_id": "d68f11f0d93a4c03b842f212c55afb7f", "model_name": "text-embedding-004", "prompt": "Seeking a paper on refining model predictions layer-by-layer in transformers with improved techniques.", "output": [["Seeking a paper on refining model predictions layer-by-layer in transformers with improved techniques.", "Title: Rewiring the Transformer with Depth-Wise LSTMs\n\nAbstract: Stacking non-linear layers allows deep neural networks to model complicated functions, and including residual connections in Transformer layers is beneficial for convergence and performance. However, residual connections may make the model \"forget\" distant layers and fail to fuse information from previous layers effectively. Selectively managing the representation aggregation of Transformer layers may lead to better performance. In this paper, we present a Transformer with depth-wise LSTMs connecting cascading Transformer layers and sub-layers. We show that layer normalization and feed-forward computation within a Transformer layer can be absorbed into depth-wise LSTMs connecting pure Transformer attention layers. Our experiments with the 6-layer Transformer show significant BLEU improvements in both WMT 14 English-German / French tasks and the OPUS-100 many-to-many multilingual NMT task, and our deep Transformer experiments demonstrate the effectiveness of depth-wise LSTM on the convergence and performance of deep Transformers."]], "corpus": "arxiv"}
+{"tstamp": 1722227232.4911, "task_type": "retrieval", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722227231.9362, "finish": 1722227232.4911, "ip": "", "conv_id": "0f67986ce75e4a2282bb74eb5e5321fb", "model_name": "text-embedding-004", "prompt": "A model improving the accuracy of location-based recommendations by 9-17% through relative spatiotemporal correlations.", "output": [["A model improving the accuracy of location-based recommendations by 9-17% through relative spatiotemporal correlations.", "Title: A Joint Two-Phase Time-Sensitive Regularized Collaborative Ranking Model for Point of Interest Recommendation\n\nAbstract: The popularity of location-based social networks (LBSNs) has led to a tremendous amount of user check-in data. Recommending points of interest (POIs) plays a key role in satisfying users' needs in LBSNs. While recent work has explored the idea of adopting collaborative ranking (CR) for recommendation, there have been few attempts to incorporate temporal information for POI recommendation using CR. In this article, we propose a two-phase CR algorithm that incorporates the geographical influence of POIs and is regularized based on the variance of POIs popularity and users' activities over time. The time-sensitive regularizer penalizes user and POIs that have been more time-sensitive in the past, helping the model to account for their long-term behavioral patterns while learning from user-POI interactions. Moreover, in the first phase, it attempts to rank visited POIs higher than the unvisited ones, and at the same time, apply the geographical influence. In the second phase, our algorithm tries to rank users' favorite POIs higher on the recommendation list. Both phases employ a collaborative learning strategy that enables the model to capture complex latent associations from two different perspectives. Experiments on real-world datasets show that our proposed time-sensitive collaborative ranking model beats state-of-the-art POI recommendation methods."]], "corpus": "arxiv"}
+{"tstamp": 1722227232.4911, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722227231.9362, "finish": 1722227232.4911, "ip": "", "conv_id": "9c26c3e84c8a4bf6ab7ce0cbedfa36d7", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "A model improving the accuracy of location-based recommendations by 9-17% through relative spatiotemporal correlations.", "output": [["A model improving the accuracy of location-based recommendations by 9-17% through relative spatiotemporal correlations.", "Title: Memory efficient location recommendation through proximity-aware representation\n\nAbstract: Sequential location recommendation plays a huge role in modern life, which can enhance user experience, bring more profit to businesses and assist in government administration. Although methods for location recommendation have evolved significantly thanks to the development of recommendation systems, there is still limited utilization of geographic information, along with the ongoing challenge of addressing data sparsity. In response, we introduce a Proximity-aware based region representation for Sequential Recommendation (PASR for short), built upon the Self-Attention Network architecture. We tackle the sparsity issue through a novel loss function employing importance sampling, which emphasizes informative negative samples during optimization. Moreover, PASR enhances the integration of geographic information by employing a self-attention-based geography encoder to the hierarchical grid and proximity grid at each GPS point. To further leverage geographic information, we utilize the proximity-aware negative samplers to enhance the quality of negative samples. We conducted evaluations using three real-world Location-Based Social Networking (LBSN) datasets, demonstrating that PASR surpasses state-of-the-art sequential location recommendation methods"]], "corpus": "arxiv"}
+{"tstamp": 1722227257.1626, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722227256.9213, "finish": 1722227257.1626, "ip": "", "conv_id": "06e0a8ed452a4140b64f3fc3ef65afc7", "model_name": "GritLM/GritLM-7B", "prompt": "can you travel to kenya with an expired passport?", "output": [["can you travel to kenya with an expired passport?", "Gaming Stackexchange\n\nQ: Can I get a document back after I have returned it? After approving a migrant worker's passport and returning it to them, I realized that it was expired. I still have their work ticket, which appears valid. Is there any way to deny them entry to the country at this point?\n\nA: Nope.\nWell, you just made a big mistake: You approved before checking everything. The game is very harsh in this regard and does not allow you to roll back your decision, once you have given back the document.\nThis is part of the message - Think before you act, even if it takes valuable time ... Or just push your luck.\nAlso: You don't get fined immediately for a simple error - you get two chances every day to make mistakes (intentional or otherwise), and then the fines start kicking in.\n"]], "corpus": "stackexchange"}
+{"tstamp": 1722227257.1626, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722227256.9213, "finish": 1722227257.1626, "ip": "", "conv_id": "18595514e0f1447ea4ec341445ee2f8d", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "can you travel to kenya with an expired passport?", "output": [["can you travel to kenya with an expired passport?", "Gaming Stackexchange\n\nQ: Can I get a document back after I have returned it? After approving a migrant worker's passport and returning it to them, I realized that it was expired. I still have their work ticket, which appears valid. Is there any way to deny them entry to the country at this point?\n\nA: Nope.\nWell, you just made a big mistake: You approved before checking everything. The game is very harsh in this regard and does not allow you to roll back your decision, once you have given back the document.\nThis is part of the message - Think before you act, even if it takes valuable time ... Or just push your luck.\nAlso: You don't get fined immediately for a simple error - you get two chances every day to make mistakes (intentional or otherwise), and then the fines start kicking in.\n"]], "corpus": "stackexchange"}
+{"tstamp": 1722227294.6746, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1722227294.2019, "finish": 1722227294.6746, "ip": "", "conv_id": "5dea31ed6e6640a48d84eccc072395b9", "model_name": "text-embedding-3-large", "prompt": "Paper on GPt models for embedding", "output": [["Paper on GPt models for embedding", "Title: Gaussian Process Topic Models\n\nAbstract: We introduce Gaussian Process Topic Models (GPTMs), a new family of topic models which can leverage a kernel among documents while extracting correlated topics. GPTMs can be considered a systematic generalization of the Correlated Topic Models (CTMs) using ideas from Gaussian Process (GP) based embedding. Since GPTMs work with both a topic covariance matrix and a document kernel matrix, learning GPTMs involves a novel component-solving a suitable Sylvester equation capturing both topic and document dependencies. The efficacy of GPTMs is demonstrated with experiments evaluating the quality of both topic modeling and embedding."]], "corpus": "arxiv"}
+{"tstamp": 1722227294.6746, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722227294.2019, "finish": 1722227294.6746, "ip": "", "conv_id": "64b61d37f88c4e7f90958d2817eca881", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "Paper on GPt models for embedding", "output": [["Paper on GPt models for embedding", "Title: Nimble GNN Embedding with Tensor-Train Decomposition\n\nAbstract: This paper describes a new method for representing embedding tables of graph neural networks (GNNs) more compactly via tensor-train (TT) decomposition. We consider the scenario where (a) the graph data that lack node features, thereby requiring the learning of embeddings during training; and (b) we wish to exploit GPU platforms, where smaller tables are needed to reduce host-to-GPU communication even for large-memory GPUs. The use of TT enables a compact parameterization of the embedding, rendering it small enough to fit entirely on modern GPUs even for massive graphs. When combined with judicious schemes for initialization and hierarchical graph partitioning, this approach can reduce the size of node embedding vectors by 1,659 times to 81,362 times on large publicly available benchmark datasets, achieving comparable or better accuracy and significant speedups on multi-GPU systems. In some cases, our model without explicit node features on input can even match the accuracy of models that use node features."]], "corpus": "arxiv"}
+{"tstamp": 1722227314.6981, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1722227314.3433, "finish": 1722227314.6981, "ip": "", "conv_id": "b47a04ca853a4603ab5765eabae923fb", "model_name": "voyage-multilingual-2", "prompt": "Work using GPT models for text embeddings", "output": [["Work using GPT models for text embeddings", "Title: SGPT: GPT Sentence Embeddings for Semantic Search\n\nAbstract: Decoder transformers have continued increasing in scale reaching hundreds of billions of parameters. Due to their scale the same decoder sets state-of-the-art results on various language tasks via prompting or fine-tuning. Yet, these large foundation models remain unusable for the related fields of semantic search and sentence embeddings. This prevents possibly new state-of-the-art results and forces organizations to train and maintain separate models. To this end, we propose SGPT to use decoders for sentence embeddings and semantic search via prompting or fine-tuning. At 5.8 billion parameters SGPT improves on the previously best sentence embeddings by a margin of 7% and outperforms a concurrent method with 175 billion parameters as measured on the BEIR search benchmark. Code, models and result files are freely available at https://github.com/Muennighoff/sgpt."]], "corpus": "arxiv"}
+{"tstamp": 1722227314.6981, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722227314.3433, "finish": 1722227314.6981, "ip": "", "conv_id": "c086e98e80db4adfb1a1ffe9e6346a15", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "Work using GPT models for text embeddings", "output": [["Work using GPT models for text embeddings", "Title: SGPT: GPT Sentence Embeddings for Semantic Search\n\nAbstract: Decoder transformers have continued increasing in scale reaching hundreds of billions of parameters. Due to their scale the same decoder sets state-of-the-art results on various language tasks via prompting or fine-tuning. Yet, these large foundation models remain unusable for the related fields of semantic search and sentence embeddings. This prevents possibly new state-of-the-art results and forces organizations to train and maintain separate models. To this end, we propose SGPT to use decoders for sentence embeddings and semantic search via prompting or fine-tuning. At 5.8 billion parameters SGPT improves on the previously best sentence embeddings by a margin of 7% and outperforms a concurrent method with 175 billion parameters as measured on the BEIR search benchmark. Code, models and result files are freely available at https://github.com/Muennighoff/sgpt."]], "corpus": "arxiv"}