Ensemble-Instruct: Generating Instruction-Tuning Data with a Heterogeneous Mixture of LMs
Paper
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2310.13961
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Published
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4
Dataset generation
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ZeroGen: Efficient Zero-shot Learning via Dataset Generation
Paper • 2202.07922 • Published • 1 -
Let's Synthesize Step by Step: Iterative Dataset Synthesis with Large Language Models by Extrapolating Errors from Small Models
Paper • 2310.13671 • Published • 18 -
Fabricator: An Open Source Toolkit for Generating Labeled Training Data with Teacher LLMs
Paper • 2309.09582 • Published • 4 -
Auto-Instruct: Automatic Instruction Generation and Ranking for Black-Box Language Models
Paper • 2310.13127 • Published • 11 -
TeGit: Generating High-Quality Instruction-Tuning Data with Text-Grounded Task Design
Paper • 2309.05447 • Published • 1 -
Ada-Instruct: Adapting Instruction Generators for Complex Reasoning
Paper • 2310.04484 • Published • 5 -
Diversity of Thought Improves Reasoning Abilities of Large Language Models
Paper • 2310.07088 • Published • 5 -
Text Data Augmentation in Low-Resource Settings via Fine-Tuning of Large Language Models
Paper • 2310.01119 • Published • 1 -
Training Generative Question-Answering on Synthetic Data Obtained from an Instruct-tuned Model
Paper • 2310.08072 • Published • 1 -
Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations
Paper • 2310.07849 • Published • 2 -
Generative Data Augmentation using LLMs improves Distributional Robustness in Question Answering
Paper • 2309.06358 • Published • 1 -
RLAIF: Scaling Reinforcement Learning from Human Feedback with AI Feedback
Paper • 2309.00267 • Published • 47 -
Adapting Large Language Models via Reading Comprehension
Paper • 2309.09530 • Published • 77 -
Self-Alignment with Instruction Backtranslation
Paper • 2308.06259 • Published • 41 -
Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor
Paper • 2212.09689 • Published • 1 -
Democratizing Reasoning Ability: Tailored Learning from Large Language Model
Paper • 2310.13332 • Published • 14 -
Teaching Language Models to Self-Improve through Interactive Demonstrations
Paper • 2310.13522 • Published • 11 -
Self-Convinced Prompting: Few-Shot Question Answering with Repeated Introspection
Paper • 2310.05035 • Published • 1 -
Tuna: Instruction Tuning using Feedback from Large Language Models
Paper • 2310.13385 • Published • 10 -
Reflection-Tuning: Data Recycling Improves LLM Instruction-Tuning
Paper • 2310.11716 • Published • 5 -
CITING: Large Language Models Create Curriculum for Instruction Tuning
Paper • 2310.02527 • Published • 2 -
AlpaGasus: Training A Better Alpaca with Fewer Data
Paper • 2307.08701 • Published • 22 -
Reverse Chain: A Generic-Rule for LLMs to Master Multi-API Planning
Paper • 2310.04474 • Published • 2 -
UltraFeedback: Boosting Language Models with High-quality Feedback
Paper • 2310.01377 • Published • 5 -
Promptor: A Conversational and Autonomous Prompt Generation Agent for Intelligent Text Entry Techniques
Paper • 2310.08101 • Published • 2 -
FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation
Paper • 2310.03214 • Published • 18 -
WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct
Paper • 2308.09583 • Published • 7 -
Retrieval-Generation Synergy Augmented Large Language Models
Paper • 2310.05149 • Published • 1 -
Prompting Large Language Models with Chain-of-Thought for Few-Shot Knowledge Base Question Generation
Paper • 2310.08395 • Published • 1 -
Prometheus: Inducing Fine-grained Evaluation Capability in Language Models
Paper • 2310.08491 • Published • 53 -
LMDX: Language Model-based Document Information Extraction and Localization
Paper • 2309.10952 • Published • 65 -
Quality-Diversity through AI Feedback
Paper • 2310.13032 • Published • 1 -
CommonCanvas: An Open Diffusion Model Trained with Creative-Commons Images
Paper • 2310.16825 • Published • 32 -
A Picture is Worth a Thousand Words: Principled Recaptioning Improves Image Generation
Paper • 2310.16656 • Published • 40 -
In-Context Pretraining: Language Modeling Beyond Document Boundaries
Paper • 2310.10638 • Published • 28 -
Large Language Models Are Also Good Prototypical Commonsense Reasoners
Paper • 2309.13165 • Published • 1 -
DialCoT Meets PPO: Decomposing and Exploring Reasoning Paths in Smaller Language Models
Paper • 2310.05074 • Published • 1 -
MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models
Paper • 2309.12284 • Published • 18 -
Commonsense Knowledge Transfer for Pre-trained Language Models
Paper • 2306.02388 • Published • 1 -
Snowman: A Million-scale Chinese Commonsense Knowledge Graph Distilled from Foundation Model
Paper • 2306.10241 • Published • 1 -
VIGC: Visual Instruction Generation and Correction
Paper • 2308.12714 • Published • 1 -
LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset
Paper • 2309.11998 • Published • 24 -
In-Context Alignment: Chat with Vanilla Language Models Before Fine-Tuning
Paper • 2308.04275 • Published • 1 -
Large Language Model as a User Simulator
Paper • 2308.11534 • Published • 2 -
Enable Language Models to Implicitly Learn Self-Improvement From Data
Paper • 2310.00898 • Published • 23 -
Textbooks Are All You Need II: phi-1.5 technical report
Paper • 2309.05463 • Published • 87 -
Aligning Large Language Models through Synthetic Feedback
Paper • 2305.13735 • Published • 1 -
Reinforced Self-Training (ReST) for Language Modeling
Paper • 2308.08998 • Published • 2 -
How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources
Paper • 2306.04751 • Published • 5 -
Query2doc: Query Expansion with Large Language Models
Paper • 2303.07678 • Published • 1 -
Query Expansion by Prompting Large Language Models
Paper • 2305.03653 • Published • 1 -
Generative Relevance Feedback with Large Language Models
Paper • 2304.13157 • Published • 1 -
InPars-v2: Large Language Models as Efficient Dataset Generators for Information Retrieval
Paper • 2301.01820 • Published • 1 -
Exploring the Viability of Synthetic Query Generation for Relevance Prediction
Paper • 2305.11944 • Published • 1 -
LM-CPPF: Paraphrasing-Guided Data Augmentation for Contrastive Prompt-Based Few-Shot Fine-Tuning
Paper • 2305.18169 • Published • 1 -
Automated Annotation with Generative AI Requires Validation
Paper • 2306.00176 • Published • 1 -
Augmented Large Language Models with Parametric Knowledge Guiding
Paper • 2305.04757 • Published • 2 -
Pre-training with Large Language Model-based Document Expansion for Dense Passage Retrieval
Paper • 2308.08285 • Published • 1 -
Learning to Retrieve In-Context Examples for Large Language Models
Paper • 2307.07164 • Published • 21 -
Tuning Language Models as Training Data Generators for Augmentation-Enhanced Few-Shot Learning
Paper • 2211.03044 • Published • 1 -
Corpus Synthesis for Zero-shot ASR domain Adaptation using Large Language Models
Paper • 2309.10707 • Published • 1 -
PromptMix: A Class Boundary Augmentation Method for Large Language Model Distillation
Paper • 2310.14192 • Published • 1 -
The Program Testing Ability of Large Language Models for Code
Paper • 2310.05727 • Published • 1 -
Assessing the potential of AI-assisted pragmatic annotation: The case of apologies
Paper • 2305.08339 • Published • 1 -
Effectiveness of Data Augmentation for Parameter Efficient Tuning with Limited Data
Paper • 2303.02577 • Published • 1 -
Rethink the Effectiveness of Text Data Augmentation: An Empirical Analysis
Paper • 2306.07664 • Published • 1 -
TeacherLM: Teaching to Fish Rather Than Giving the Fish, Language Modeling Likewise
Paper • 2310.19019 • Published • 9 -
Textbooks Are All You Need
Paper • 2306.11644 • Published • 142 -
Connecting Large Language Models with Evolutionary Algorithms Yields Powerful Prompt Optimizers
Paper • 2309.08532 • Published • 52 -
SAIL: Search-Augmented Instruction Learning
Paper • 2305.15225 • Published • 2 -
Reproducing Whisper-Style Training Using an Open-Source Toolkit and Publicly Available Data
Paper • 2309.13876 • Published • 1 -
Leveraging Training Data in Few-Shot Prompting for Numerical Reasoning
Paper • 2305.18170 • Published • 2 -
Constructing Multilingual Code Search Dataset Using Neural Machine Translation
Paper • 2306.15604 • Published • 1 -
TRACED: Execution-aware Pre-training for Source Code
Paper • 2306.07487 • Published • 1 -
Too Few Bug Reports? Exploring Data Augmentation for Improved Changeset-based Bug Localization
Paper • 2305.16430 • Published • 1 -
Learning to Reason and Memorize with Self-Notes
Paper • 2305.00833 • Published • 4 -
Generating Efficient Training Data via LLM-based Attribute Manipulation
Paper • 2307.07099 • Published • 1 -
End-to-end Knowledge Retrieval with Multi-modal Queries
Paper • 2306.00424 • Published • 1 -
EchoPrompt: Instructing the Model to Rephrase Queries for Improved In-context Learning
Paper • 2309.10687 • Published • 1 -
AugGPT: Leveraging ChatGPT for Text Data Augmentation
Paper • 2302.13007 • Published • 1 -
Large Language Models as Annotators: Enhancing Generalization of NLP Models at Minimal Cost
Paper • 2306.15766 • Published • 1 -
Quick Starting Dialog Systems with Paraphrase Generation
Paper • 2204.02546 • Published • 1 -
NLP From Scratch Without Large-Scale Pretraining: A Simple and Efficient Framework
Paper • 2111.04130 • Published • 1 -
Scaling Relationship on Learning Mathematical Reasoning with Large Language Models
Paper • 2308.01825 • Published • 21 -
Harnessing the Power of David against Goliath: Exploring Instruction Data Generation without Using Closed-Source Models
Paper • 2308.12711 • Published • 1 -
AnnoLLM: Making Large Language Models to Be Better Crowdsourced Annotators
Paper • 2303.16854 • Published • 1 -
Training Language Models with Language Feedback at Scale
Paper • 2303.16755 • Published • 1 -
Magicoder: Source Code Is All You Need
Paper • 2312.02120 • Published • 79 -
Asking Questions the Human Way: Scalable Question-Answer Generation from Text Corpus
Paper • 2002.00748 • Published • 1 -
Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models
Paper • 2312.06585 • Published • 28 -
WaveCoder: Widespread And Versatile Enhanced Instruction Tuning with Refined Data Generation
Paper • 2312.14187 • Published • 49 -
Self-Instruct: Aligning Language Model with Self Generated Instructions
Paper • 2212.10560 • Published • 8 -
WizardLM: Empowering Large Language Models to Follow Complex Instructions
Paper • 2304.12244 • Published • 13 -
WizardCoder: Empowering Code Large Language Models with Evol-Instruct
Paper • 2306.08568 • Published • 28 -
EASYTOOL: Enhancing LLM-based Agents with Concise Tool Instruction
Paper • 2401.06201 • Published • 2 -
AceCoder: Utilizing Existing Code to Enhance Code Generation
Paper • 2303.17780 • Published • 1 -
SPADE: Synthesizing Assertions for Large Language Model Pipelines
Paper • 2401.03038 • Published • 2 -
Mixture of Soft Prompts for Controllable Data Generation
Paper • 2303.01580 • Published • 1 -
Rephrasing the Web: A Recipe for Compute and Data-Efficient Language Modeling
Paper • 2401.16380 • Published • 48 -
Improving Text Embeddings with Large Language Models
Paper • 2401.00368 • Published • 79 -
CooK: Empowering General-Purpose Language Models with Modular and Collaborative Knowledge
Paper • 2305.09955 • Published • 1 -
DataDreamer: A Tool for Synthetic Data Generation and Reproducible LLM Workflows
Paper • 2402.10379 • Published • 30 -
Pre-trained Language Models as Re-Annotators
Paper • 2205.05368 • Published • 1 -
A Morphologically-Aware Dictionary-based Data Augmentation Technique for Machine Translation of Under-Represented Languages
Paper • 2402.01939 • Published • 1 -
Multimodal ArXiv: A Dataset for Improving Scientific Comprehension of Large Vision-Language Models
Paper • 2403.00231 • Published • 1 -
GECTurk: Grammatical Error Correction and Detection Dataset for Turkish
Paper • 2309.11346 • Published -
LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement
Paper • 2403.15042 • Published • 25 -
MathGenie: Generating Synthetic Data with Question Back-translation for Enhancing Mathematical Reasoning of LLMs
Paper • 2402.16352 • Published • 1 -
OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset
Paper • 2402.10176 • Published • 35 -
CodecLM: Aligning Language Models with Tailored Synthetic Data
Paper • 2404.05875 • Published • 16 -
NExT: Teaching Large Language Models to Reason about Code Execution
Paper • 2404.14662 • Published • 4 -
Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptation
Paper • 2402.18334 • Published • 12 -
GeMQuAD : Generating Multilingual Question Answering Datasets from Large Language Models using Few Shot Learning
Paper • 2404.09163 • Published -
Better Synthetic Data by Retrieving and Transforming Existing Datasets
Paper • 2404.14361 • Published • 1 -
DUQGen: Effective Unsupervised Domain Adaptation of Neural Rankers by Diversifying Synthetic Query Generation
Paper • 2404.02489 • Published -
Prompting-based Synthetic Data Generation for Few-Shot Question Answering
Paper • 2405.09335 • Published -
SynthesizRR: Generating Diverse Datasets with Retrieval Augmentation
Paper • 2405.10040 • Published -
DeepSeek-Prover: Advancing Theorem Proving in LLMs through Large-Scale Synthetic Data
Paper • 2405.14333 • Published • 35 -
Grounding Data Science Code Generation with Input-Output Specifications
Paper • 2402.08073 • Published -
AgentTuning: Enabling Generalized Agent Abilities for LLMs
Paper • 2310.12823 • Published • 35 -
SemCoder: Training Code Language Models with Comprehensive Semantics
Paper • 2406.01006 • Published -
CrossTune: Black-Box Few-Shot Classification with Label Enhancement
Paper • 2403.12468 • Published -
TarGEN: Targeted Data Generation with Large Language Models
Paper • 2310.17876 • Published -
Enhancing Conversational Search: Large Language Model-Aided Informative Query Rewriting
Paper • 2310.09716 • Published -
Automatically Generating Numerous Context-Driven SFT Data for LLMs across Diverse Granularity
Paper • 2405.16579 • Published