Scheduled Commit

data/retrieval_battle-b9229914-47bc-4da8-a21b-89329fff8207.jsonl

@@ -25,3 +25,7 @@
 {"tstamp": 1722311183.6493, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "344ba32a2d4e44d4857f9fcc603f8ef8", "0_model_name": "sentence-transformers/all-MiniLM-L6-v2", "0_prompt": "What are the differences between machine learning and deep learning?", "0_output": [["What are the differences between machine learning and deep learning?", "Comparison of deep learning software\n\nThe following table compares notable software frameworks, libraries and computer programs for deep learning.\nDeep-learning software by name\nComparison of compatibility of machine learning models"]], "0_corpus": "wikipedia", "1_conv_id": "b3eb01f72a77480caae9a4025986769f", "1_model_name": "voyage-multilingual-2", "1_prompt": "What are the differences between machine learning and deep learning?", "1_output": [["What are the differences between machine learning and deep learning?", "Deep learning\n\nOverview Most modern deep learning models are based on multi-layered neural networks such as convolutional neural networks and transformers, although they can also include propositional formulas or latent variables organized layer-wise in deep generative models such as the nodes in deep belief networks and deep Boltzmann machines.\nFundamentally, deep learning refers to a class of machine learning algorithms in which a hierarchy of layers is used to transform input data into a slightly more abstract and composite representation. For example, in an image recognition model, the raw input may be an image (represented as a tensor of pixels). The first representational layer may attempt to identify basic shapes such as lines and circles, the second layer may compose and encode arrangements of edges, the third layer may encode a nose and eyes, and the fourth layer may recognize that the image contains a face.\nImportantly, a deep learning process can learn which features to optimally place in which level on its own. Prior to deep learning, machine learning techniques often involved hand-crafted feature engineering to transform the data into a more suitable representation for a classification algorithm to operate upon. In the deep learning approach, features are not hand-crafted and the model discovers useful feature representations from the data automatically. This does not eliminate the need for hand-tuning; for example, varying numbers of layers and layer sizes can provide different degrees of abstraction."]], "1_corpus": "wikipedia"}
 {"tstamp": 1722311199.5255, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "92e783c936084f088d8757c497e00510", "0_model_name": "intfloat/multilingual-e5-large-instruct", "0_prompt": "are electrical outlets on studs?", "0_output": [["are electrical outlets on studs?", "Electronics Stackexchange\n\nQ: Purpose of breadboard \"studs\" on base off micro breadboards? I recently bought some micro breadboards on Ebay.  Extremely useful little things.  However each have two awkward studs on their bases.  See the attached photo.   What I'm curious about is their purpose?  What might these \"plugin\" to or be used for?  I think I'm going to file them off.\n\n\nA: They connect to a base that fixes them with regards to each other.\n\n"]], "0_corpus": "stackexchange", "1_conv_id": "5a38498ae0f7449186feb5beb2f13faf", "1_model_name": "sentence-transformers/all-MiniLM-L6-v2", "1_prompt": "are electrical outlets on studs?", "1_output": [["are electrical outlets on studs?", "Math Stackexchange\n\nQ: Write $\\cos^2(x)$ as linear combination of $x \\mapsto \\sin(x)$ and $x \\mapsto \\cos(x)$ \nCan we write $\\cos^2(x)$ as linear combination of $x \\mapsto \\sin(x)$ and  $x \\mapsto \\cos(x)$?\n\nI know\n$$\n\\cos^2(x)\n= \\frac{\\cos(2x) + 1}{2}\n= 1 - \\sin^2(x)\n= \\cos(2x) + \\sin^2(x)\n$$\nbut none of these helped.\nThen, I tried to solve\n$$\n\\cos^2(x) = \\alpha \\sin(x) + \\beta \\cos(x)\n$$\nfor the coefficients $\\alpha, \\beta \\in \\mathbb{R}$.\nBut when plugging in $x = 0$ I get $\\beta = 1$ and for $x = \\frac{\\pi}{2}$ I get $\\alpha = 0$. Plugging those values back in I obtain a false statement, and WolframAlpha can't do better!\nThis is from a numerical analysis exam and the second function is $x \\mapsto \\sqrt{2}\\cos\\left(\\frac{\\pi}{4} - x \\right)$, which can easily be expressed in terms of $x \\mapsto \\sin(x)$ and $x \\mapsto \\cos(x)$ by the corresponding addition formula.\n\nA: The function $f(x):=\\cos^2 x$ has $f(x+\\pi)\\equiv f(x)$, but any linear combination $g$ of $\\cos$ and $\\sin$ has $g(x+\\pi)\\equiv -g(x)$.\n"]], "1_corpus": "stackexchange"}
 {"tstamp": 1722311222.231, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "de94bd25010e43bda926db85c7971b20", "0_model_name": "text-embedding-3-large", "0_prompt": "when to use an air gap for dishwasher", "0_output": [["when to use an air gap for dishwasher", "Air gap (networking)\n\nAn air gap, air wall, air gapping or disconnected network is a network security measure employed on one or more computers to ensure that a secure computer network is physically isolated from unsecured networks, such as the public Internet or an unsecured local area network. It means a computer or network has no network interface controllers connected to other networks, with a physical or conceptual air gap, analogous to the air gap used in plumbing to maintain water quality."]], "0_corpus": "wikipedia", "1_conv_id": "436c2a44d65c4bb68f57a571cada2260", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": "when to use an air gap for dishwasher", "1_output": [["when to use an air gap for dishwasher", "Air gap (networking)\n\nAn air gap, air wall, air gapping or disconnected network is a network security measure employed on one or more computers to ensure that a secure computer network is physically isolated from unsecured networks, such as the public Internet or an unsecured local area network. It means a computer or network has no network interface controllers connected to other networks, with a physical or conceptual air gap, analogous to the air gap used in plumbing to maintain water quality."]], "1_corpus": "wikipedia"}
+{"tstamp": 1722311256.1494, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "8685880d067944afa0b8593df3edca7c", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "A paper on scalable and accurate graph transformers using sparse attention and expander graphs", "0_output": [["A paper on scalable and accurate graph transformers using sparse attention and expander graphs", "Title: Exphormer: Sparse Transformers for Graphs\n\nAbstract: Graph transformers have emerged as a promising architecture for a variety of graph learning and representation tasks. Despite their successes, though, it remains challenging to scale graph transformers to large graphs while maintaining accuracy competitive with message-passing networks. In this paper, we introduce Exphormer, a framework for building powerful and scalable graph transformers. Exphormer consists of a sparse attention mechanism based on two mechanisms: virtual global nodes and expander graphs, whose mathematical characteristics, such as spectral expansion, pseduorandomness, and sparsity, yield graph transformers with complexity only linear in the size of the graph, while allowing us to prove desirable theoretical properties of the resulting transformer models. We show that incorporating Exphormer into the recently-proposed GraphGPS framework produces models with competitive empirical results on a wide variety of graph datasets, including state-of-the-art results on three datasets. We also show that Exphormer can scale to datasets on larger graphs than shown in previous graph transformer architectures. Code can be found at \\url{https://github.com/hamed1375/Exphormer}."]], "0_corpus": "arxiv", "1_conv_id": "e515fe1b4bad484eba5701f6c8446cc0", "1_model_name": "intfloat/multilingual-e5-large-instruct", "1_prompt": "A paper on scalable and accurate graph transformers using sparse attention and expander graphs", "1_output": [["A paper on scalable and accurate graph transformers using sparse attention and expander graphs", "Title: Deformable Graph Transformer\n\nAbstract: Transformer-based models have recently shown success in representation learning on graph-structured data beyond natural language processing and computer vision. However, the success is limited to small-scale graphs due to the drawbacks of full dot-product attention on graphs such as the quadratic complexity with respect to the number of nodes and message aggregation from enormous irrelevant nodes. To address these issues, we propose Deformable Graph Transformer (DGT) that performs sparse attention via dynamically sampled relevant nodes for efficiently handling large-scale graphs with a linear complexity in the number of nodes. Specifically, our framework first constructs multiple node sequences with various criteria to consider both structural and semantic proximity. Then, combining with our learnable Katz Positional Encodings, the sparse attention is applied to the node sequences for learning node representations with a significantly reduced computational cost. Extensive experiments demonstrate that our DGT achieves state-of-the-art performance on 7 graph benchmark datasets with 2.5 - 449 times less computational cost compared to transformer-based graph models with full attention."]], "1_corpus": "arxiv"}
+{"tstamp": 1722311284.3104, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "f4c9ac94173d4247865eb70981697a10", "0_model_name": "mixedbread-ai/mxbai-embed-large-v1", "0_prompt": "What are the key principles of quantum mechanics?", "0_output": [["What are the key principles of quantum mechanics?", "Quantum mechanics\n\nQuantum mechanics is a fundamental theory in physics that describes the behavior of nature at and below the scale of atoms. It is the foundation of all quantum physics, which includes quantum chemistry, quantum field theory, quantum technology, and quantum information science.\nQuantum mechanics can describe many systems that classical physics cannot. Classical physics can describe many aspects of nature at an ordinary (macroscopic and (optical) microscopic) scale, but is not sufficient for describing them at very small submicroscopic (atomic and subatomic) scales. Most theories in classical physics can be derived from quantum mechanics as an approximation valid at large (macroscopic/microscopic) scale.\nQuantum systems have bound states that are quantized to discrete values of energy, momentum, angular momentum, and other quantities, in contrast to classical systems where these quantities can be measured continuously. Measurements of quantum systems show characteristics of both particles and waves (wave–particle duality), and there are limits to how accurately the value of a physical quantity can be predicted prior to its measurement, given a complete set of initial conditions (the uncertainty principle)."]], "0_corpus": "wikipedia", "1_conv_id": "8ed959dba71e4b10b407a68b0b7a8ca9", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": "What are the key principles of quantum mechanics?", "1_output": [["What are the key principles of quantum mechanics?", "Introduction to quantum mechanics\n\nMany aspects of quantum mechanics are counterintuitive and can seem paradoxical because they describe behavior quite different from that seen at larger scales. In the words of quantum physicist Richard Feynman, quantum mechanics deals with \"nature as She is—absurd\". Features of quantum mechanics often defy simple explanations in everyday language. One example of this is the uncertainty principle: precise measurements of position cannot be combined with precise measurements of velocity. Another example is entanglement: a measurement made on one particle (such as an electron that is measured to have spin 'up') will correlate with a measurement on a second particle (an electron will be found to have spin 'down') if the two particles have a shared history. This will apply even if it is impossible for the result of the first measurement to have been transmitted to the second particle before the second measurement takes place.\nQuantum mechanics helps us understand chemistry, because it explains how atoms interact with each other and form molecules. Many remarkable phenomena can be explained using quantum mechanics, like superfluidity. For example, if liquid helium cooled to a temperature near absolute zero is placed in a container, it spontaneously flows up and over the rim of its container; this is an effect which cannot be explained by classical physics.\nHistory\nJames C. Maxwell's unification of the equations governing electricity, magnetism, and light in the late 19th century led to experiments on the interaction of light and matter. Some of these experiments had aspects which could not be explained until quantum mechanics emerged in the early part of the 20th century.\nEvidence of quanta from the photoelectric effect"]], "1_corpus": "wikipedia"}
+{"tstamp": 1722311301.4896, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "e25ddbeab37a4e65805ee17fe8dbf844", "0_model_name": "intfloat/multilingual-e5-large-instruct", "0_prompt": "What is the history and significance of the Silk Road?", "0_output": [["What is the history and significance of the Silk Road?", "Silk Road\n\nThe Silk Road was a network of Eurasian trade routes active from the second century BCE until the mid-15th century. Spanning over , it played a central role in facilitating economic, cultural, political, and religious interactions between the Eastern and Western worlds. The name \"Silk Road\" was first coined in the late 19th century, but some 20th- and 21st-century historians instead prefer the term Silk Routes, on the grounds that it more accurately describes the intricate web of land and sea routes connecting Central, East, South, Southeast, and West Asia as well as East Africa and Southern Europe."]], "0_corpus": "wikipedia", "1_conv_id": "f903812140fb434d886b47c056c458bf", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "What is the history and significance of the Silk Road?", "1_output": [["What is the history and significance of the Silk Road?", "Silk Road\n\nThe Silk Road was a network of Eurasian trade routes active from the second century BCE until the mid-15th century. Spanning over , it played a central role in facilitating economic, cultural, political, and religious interactions between the Eastern and Western worlds. The name \"Silk Road\" was first coined in the late 19th century, but some 20th- and 21st-century historians instead prefer the term Silk Routes, on the grounds that it more accurately describes the intricate web of land and sea routes connecting Central, East, South, Southeast, and West Asia as well as East Africa and Southern Europe."]], "1_corpus": "wikipedia"}
+{"tstamp": 1722311318.7266, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "c1f62d7555804ab3a153de69ca106e31", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "How do black holes form and what are their main properties?", "0_output": [["How do black holes form and what are their main properties?", "Black hole\n\nFormation and evolution Given the bizarre character of black holes, it was long questioned whether such objects could actually exist in nature or whether they were merely pathological solutions to Einstein's equations. Einstein himself wrongly thought black holes would not form, because he held that the angular momentum of collapsing particles would stabilize their motion at some radius. This led the general relativity community to dismiss all results to the contrary for many years. However, a minority of relativists continued to contend that black holes were physical objects, and by the end of the 1960s, they had persuaded the majority of researchers in the field that there is no obstacle to the formation of an event horizon.\nPenrose demonstrated that once an event horizon forms, general relativity without quantum mechanics requires that a singularity will form within. Shortly afterwards, Hawking showed that many cosmological solutions that describe the Big Bang have singularities without scalar fields or other exotic matter. The Kerr solution, the no-hair theorem, and the laws of black hole thermodynamics showed that the physical properties of black holes were simple and comprehensible, making them respectable subjects for research. Conventional black holes are formed by gravitational collapse of heavy objects such as stars, but they can also in theory be formed by other processes."]], "0_corpus": "wikipedia", "1_conv_id": "1c02620544ee43d78b4c741a10b183e6", "1_model_name": "text-embedding-3-large", "1_prompt": "How do black holes form and what are their main properties?", "1_output": [["How do black holes form and what are their main properties?", "Black hole\n\nFormation and evolution Given the bizarre character of black holes, it was long questioned whether such objects could actually exist in nature or whether they were merely pathological solutions to Einstein's equations. Einstein himself wrongly thought black holes would not form, because he held that the angular momentum of collapsing particles would stabilize their motion at some radius. This led the general relativity community to dismiss all results to the contrary for many years. However, a minority of relativists continued to contend that black holes were physical objects, and by the end of the 1960s, they had persuaded the majority of researchers in the field that there is no obstacle to the formation of an event horizon.\nPenrose demonstrated that once an event horizon forms, general relativity without quantum mechanics requires that a singularity will form within. Shortly afterwards, Hawking showed that many cosmological solutions that describe the Big Bang have singularities without scalar fields or other exotic matter. The Kerr solution, the no-hair theorem, and the laws of black hole thermodynamics showed that the physical properties of black holes were simple and comprehensible, making them respectable subjects for research. Conventional black holes are formed by gravitational collapse of heavy objects such as stars, but they can also in theory be formed by other processes."]], "1_corpus": "wikipedia"}

data/retrieval_individual-b9229914-47bc-4da8-a21b-89329fff8207.jsonl

@@ -93,3 +93,13 @@
 {"tstamp": 1722311174.9589, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1722311174.5805, "finish": 1722311174.9589, "ip": "", "conv_id": "b3eb01f72a77480caae9a4025986769f", "model_name": "voyage-multilingual-2", "prompt": "What are the differences between machine learning and deep learning?", "output": [["What are the differences between machine learning and deep learning?", "Deep learning\n\nOverview Most modern deep learning models are based on multi-layered neural networks such as convolutional neural networks and transformers, although they can also include propositional formulas or latent variables organized layer-wise in deep generative models such as the nodes in deep belief networks and deep Boltzmann machines.\nFundamentally, deep learning refers to a class of machine learning algorithms in which a hierarchy of layers is used to transform input data into a slightly more abstract and composite representation. For example, in an image recognition model, the raw input may be an image (represented as a tensor of pixels). The first representational layer may attempt to identify basic shapes such as lines and circles, the second layer may compose and encode arrangements of edges, the third layer may encode a nose and eyes, and the fourth layer may recognize that the image contains a face.\nImportantly, a deep learning process can learn which features to optimally place in which level on its own. Prior to deep learning, machine learning techniques often involved hand-crafted feature engineering to transform the data into a more suitable representation for a classification algorithm to operate upon. In the deep learning approach, features are not hand-crafted and the model discovers useful feature representations from the data automatically. This does not eliminate the need for hand-tuning; for example, varying numbers of layers and layer sizes can provide different degrees of abstraction."]], "corpus": "wikipedia"}
 {"tstamp": 1722311209.641, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1722311207.0891, "finish": 1722311209.641, "ip": "", "conv_id": "de94bd25010e43bda926db85c7971b20", "model_name": "text-embedding-3-large", "prompt": "when to use an air gap for dishwasher", "output": [["when to use an air gap for dishwasher", "Air gap (networking)\n\nAn air gap, air wall, air gapping or disconnected network is a network security measure employed on one or more computers to ensure that a secure computer network is physically isolated from unsecured networks, such as the public Internet or an unsecured local area network. It means a computer or network has no network interface controllers connected to other networks, with a physical or conceptual air gap, analogous to the air gap used in plumbing to maintain water quality."]], "corpus": "wikipedia"}
 {"tstamp": 1722311209.641, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1722311207.0891, "finish": 1722311209.641, "ip": "", "conv_id": "436c2a44d65c4bb68f57a571cada2260", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "when to use an air gap for dishwasher", "output": [["when to use an air gap for dishwasher", "Air gap (networking)\n\nAn air gap, air wall, air gapping or disconnected network is a network security measure employed on one or more computers to ensure that a secure computer network is physically isolated from unsecured networks, such as the public Internet or an unsecured local area network. It means a computer or network has no network interface controllers connected to other networks, with a physical or conceptual air gap, analogous to the air gap used in plumbing to maintain water quality."]], "corpus": "wikipedia"}
+{"tstamp": 1722311232.4959, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722311232.3107, "finish": 1722311232.4959, "ip": "", "conv_id": "8685880d067944afa0b8593df3edca7c", "model_name": "GritLM/GritLM-7B", "prompt": "A paper on scalable and accurate graph transformers using sparse attention and expander graphs", "output": [["A paper on scalable and accurate graph transformers using sparse attention and expander graphs", "Title: Exphormer: Sparse Transformers for Graphs\n\nAbstract: Graph transformers have emerged as a promising architecture for a variety of graph learning and representation tasks. Despite their successes, though, it remains challenging to scale graph transformers to large graphs while maintaining accuracy competitive with message-passing networks. In this paper, we introduce Exphormer, a framework for building powerful and scalable graph transformers. Exphormer consists of a sparse attention mechanism based on two mechanisms: virtual global nodes and expander graphs, whose mathematical characteristics, such as spectral expansion, pseduorandomness, and sparsity, yield graph transformers with complexity only linear in the size of the graph, while allowing us to prove desirable theoretical properties of the resulting transformer models. We show that incorporating Exphormer into the recently-proposed GraphGPS framework produces models with competitive empirical results on a wide variety of graph datasets, including state-of-the-art results on three datasets. We also show that Exphormer can scale to datasets on larger graphs than shown in previous graph transformer architectures. Code can be found at \\url{https://github.com/hamed1375/Exphormer}."]], "corpus": "arxiv"}
+{"tstamp": 1722311232.4959, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722311232.3107, "finish": 1722311232.4959, "ip": "", "conv_id": "e515fe1b4bad484eba5701f6c8446cc0", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "A paper on scalable and accurate graph transformers using sparse attention and expander graphs", "output": [["A paper on scalable and accurate graph transformers using sparse attention and expander graphs", "Title: Deformable Graph Transformer\n\nAbstract: Transformer-based models have recently shown success in representation learning on graph-structured data beyond natural language processing and computer vision. However, the success is limited to small-scale graphs due to the drawbacks of full dot-product attention on graphs such as the quadratic complexity with respect to the number of nodes and message aggregation from enormous irrelevant nodes. To address these issues, we propose Deformable Graph Transformer (DGT) that performs sparse attention via dynamically sampled relevant nodes for efficiently handling large-scale graphs with a linear complexity in the number of nodes. Specifically, our framework first constructs multiple node sequences with various criteria to consider both structural and semantic proximity. Then, combining with our learnable Katz Positional Encodings, the sparse attention is applied to the node sequences for learning node representations with a significantly reduced computational cost. Extensive experiments demonstrate that our DGT achieves state-of-the-art performance on 7 graph benchmark datasets with 2.5 - 449 times less computational cost compared to transformer-based graph models with full attention."]], "corpus": "arxiv"}
+{"tstamp": 1722311273.9209, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722311273.7354, "finish": 1722311273.9209, "ip": "", "conv_id": "f4c9ac94173d4247865eb70981697a10", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "What are the key principles of quantum mechanics?", "output": [["What are the key principles of quantum mechanics?", "Quantum mechanics\n\nQuantum mechanics is a fundamental theory in physics that describes the behavior of nature at and below the scale of atoms. It is the foundation of all quantum physics, which includes quantum chemistry, quantum field theory, quantum technology, and quantum information science.\nQuantum mechanics can describe many systems that classical physics cannot. Classical physics can describe many aspects of nature at an ordinary (macroscopic and (optical) microscopic) scale, but is not sufficient for describing them at very small submicroscopic (atomic and subatomic) scales. Most theories in classical physics can be derived from quantum mechanics as an approximation valid at large (macroscopic/microscopic) scale.\nQuantum systems have bound states that are quantized to discrete values of energy, momentum, angular momentum, and other quantities, in contrast to classical systems where these quantities can be measured continuously. Measurements of quantum systems show characteristics of both particles and waves (wave–particle duality), and there are limits to how accurately the value of a physical quantity can be predicted prior to its measurement, given a complete set of initial conditions (the uncertainty principle)."]], "corpus": "wikipedia"}
+{"tstamp": 1722311273.9209, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1722311273.7354, "finish": 1722311273.9209, "ip": "", "conv_id": "8ed959dba71e4b10b407a68b0b7a8ca9", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "What are the key principles of quantum mechanics?", "output": [["What are the key principles of quantum mechanics?", "Introduction to quantum mechanics\n\nMany aspects of quantum mechanics are counterintuitive and can seem paradoxical because they describe behavior quite different from that seen at larger scales. In the words of quantum physicist Richard Feynman, quantum mechanics deals with \"nature as She is—absurd\". Features of quantum mechanics often defy simple explanations in everyday language. One example of this is the uncertainty principle: precise measurements of position cannot be combined with precise measurements of velocity. Another example is entanglement: a measurement made on one particle (such as an electron that is measured to have spin 'up') will correlate with a measurement on a second particle (an electron will be found to have spin 'down') if the two particles have a shared history. This will apply even if it is impossible for the result of the first measurement to have been transmitted to the second particle before the second measurement takes place.\nQuantum mechanics helps us understand chemistry, because it explains how atoms interact with each other and form molecules. Many remarkable phenomena can be explained using quantum mechanics, like superfluidity. For example, if liquid helium cooled to a temperature near absolute zero is placed in a container, it spontaneously flows up and over the rim of its container; this is an effect which cannot be explained by classical physics.\nHistory\nJames C. Maxwell's unification of the equations governing electricity, magnetism, and light in the late 19th century led to experiments on the interaction of light and matter. Some of these experiments had aspects which could not be explained until quantum mechanics emerged in the early part of the 20th century.\nEvidence of quanta from the photoelectric effect"]], "corpus": "wikipedia"}
+{"tstamp": 1722311298.8755, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722311298.7354, "finish": 1722311298.8755, "ip": "", "conv_id": "e25ddbeab37a4e65805ee17fe8dbf844", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "What is the history and significance of the Silk Road?", "output": [["What is the history and significance of the Silk Road?", "Silk Road\n\nThe Silk Road was a network of Eurasian trade routes active from the second century BCE until the mid-15th century. Spanning over , it played a central role in facilitating economic, cultural, political, and religious interactions between the Eastern and Western worlds. The name \"Silk Road\" was first coined in the late 19th century, but some 20th- and 21st-century historians instead prefer the term Silk Routes, on the grounds that it more accurately describes the intricate web of land and sea routes connecting Central, East, South, Southeast, and West Asia as well as East Africa and Southern Europe."]], "corpus": "wikipedia"}
+{"tstamp": 1722311298.8755, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722311298.7354, "finish": 1722311298.8755, "ip": "", "conv_id": "f903812140fb434d886b47c056c458bf", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "What is the history and significance of the Silk Road?", "output": [["What is the history and significance of the Silk Road?", "Silk Road\n\nThe Silk Road was a network of Eurasian trade routes active from the second century BCE until the mid-15th century. Spanning over , it played a central role in facilitating economic, cultural, political, and religious interactions between the Eastern and Western worlds. The name \"Silk Road\" was first coined in the late 19th century, but some 20th- and 21st-century historians instead prefer the term Silk Routes, on the grounds that it more accurately describes the intricate web of land and sea routes connecting Central, East, South, Southeast, and West Asia as well as East Africa and Southern Europe."]], "corpus": "wikipedia"}
+{"tstamp": 1722311315.3188, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722311314.8171, "finish": 1722311315.3188, "ip": "", "conv_id": "c1f62d7555804ab3a153de69ca106e31", "model_name": "GritLM/GritLM-7B", "prompt": "How do black holes form and what are their main properties?", "output": [["How do black holes form and what are their main properties?", "Black hole\n\nFormation and evolution Given the bizarre character of black holes, it was long questioned whether such objects could actually exist in nature or whether they were merely pathological solutions to Einstein's equations. Einstein himself wrongly thought black holes would not form, because he held that the angular momentum of collapsing particles would stabilize their motion at some radius. This led the general relativity community to dismiss all results to the contrary for many years. However, a minority of relativists continued to contend that black holes were physical objects, and by the end of the 1960s, they had persuaded the majority of researchers in the field that there is no obstacle to the formation of an event horizon.\nPenrose demonstrated that once an event horizon forms, general relativity without quantum mechanics requires that a singularity will form within. Shortly afterwards, Hawking showed that many cosmological solutions that describe the Big Bang have singularities without scalar fields or other exotic matter. The Kerr solution, the no-hair theorem, and the laws of black hole thermodynamics showed that the physical properties of black holes were simple and comprehensible, making them respectable subjects for research. Conventional black holes are formed by gravitational collapse of heavy objects such as stars, but they can also in theory be formed by other processes."]], "corpus": "wikipedia"}
+{"tstamp": 1722311315.3188, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1722311314.8171, "finish": 1722311315.3188, "ip": "", "conv_id": "1c02620544ee43d78b4c741a10b183e6", "model_name": "text-embedding-3-large", "prompt": "How do black holes form and what are their main properties?", "output": [["How do black holes form and what are their main properties?", "Black hole\n\nFormation and evolution Given the bizarre character of black holes, it was long questioned whether such objects could actually exist in nature or whether they were merely pathological solutions to Einstein's equations. Einstein himself wrongly thought black holes would not form, because he held that the angular momentum of collapsing particles would stabilize their motion at some radius. This led the general relativity community to dismiss all results to the contrary for many years. However, a minority of relativists continued to contend that black holes were physical objects, and by the end of the 1960s, they had persuaded the majority of researchers in the field that there is no obstacle to the formation of an event horizon.\nPenrose demonstrated that once an event horizon forms, general relativity without quantum mechanics requires that a singularity will form within. Shortly afterwards, Hawking showed that many cosmological solutions that describe the Big Bang have singularities without scalar fields or other exotic matter. The Kerr solution, the no-hair theorem, and the laws of black hole thermodynamics showed that the physical properties of black holes were simple and comprehensible, making them respectable subjects for research. Conventional black holes are formed by gravitational collapse of heavy objects such as stars, but they can also in theory be formed by other processes."]], "corpus": "wikipedia"}
+{"tstamp": 1722311338.9735, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1722311338.5046, "finish": 1722311338.9735, "ip": "", "conv_id": "6db021a59686456b856a8b96bb3ccc8b", "model_name": "text-embedding-3-large", "prompt": "What are the implications of Gödel’s incompleteness theorems on the foundations of mathematics and computer science?", "output": [["What are the implications of Gödel’s incompleteness theorems on the foundations of mathematics and computer science?", "Gödel's incompleteness theorems\n\nObserve then, that if we can prove that the system  is consistent (ie. the statement in the hypothesis of ), then we have proved that  is not provable. But this is a contradiction since by the 1st Incompleteness Theorem, this sentence (ie. what is implied in the sentence , \"\"\" is not provable\") is what we construct to be unprovable. Notice that this is why we require formalizing the first Incompleteness Theorem in : to prove the 2nd Incompleteness Theorem, we obtain a contradiction with the 1st Incompleteness Theorem which can do only by showing that the theorem holds in . So we cannot prove that the system  is consistent. And the 2nd Incompleteness Theorem statement follows.\nDiscussion and implications\nThe incompleteness results affect the philosophy of mathematics, particularly versions of formalism, which use a single system of formal logic to define their principles.\nConsequences for logicism and Hilbert's second problem\nThe incompleteness theorem is sometimes thought to have severe consequences for the program of logicism proposed by Gottlob Frege and Bertrand Russell, which aimed to define the natural numbers in terms of logic. Bob Hale and Crispin Wright argue that it is not a problem for logicism because the incompleteness theorems apply equally to first-order logic as they do to arithmetic. They argue that only those who believe that the natural numbers are to be defined in terms of first order logic have this problem."]], "corpus": "wikipedia"}
+{"tstamp": 1722311338.9735, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722311338.5046, "finish": 1722311338.9735, "ip": "", "conv_id": "de0a7e21ceeb42dc94708bac0371c952", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "What are the implications of Gödel’s incompleteness theorems on the foundations of mathematics and computer science?", "output": [["What are the implications of Gödel’s incompleteness theorems on the foundations of mathematics and computer science?", "Gödel's incompleteness theorems\n\nGödel's incompleteness theorems are two theorems of mathematical logic that are concerned with the limits of  in formal axiomatic theories. These results, published by Kurt Gödel in 1931, are important both in mathematical logic and in the philosophy of mathematics. The theorems are widely, but not universally, interpreted as showing that Hilbert's program to find a complete and consistent set of axioms for all mathematics is impossible.\nThe first incompleteness theorem states that no consistent system of axioms whose theorems can be listed by an effective procedure (i.e. an algorithm) is capable of proving all truths about the arithmetic of natural numbers.  For any such consistent formal system, there will always be statements about natural numbers that are true, but that are unprovable within the system.\nThe second incompleteness theorem, an extension of the first, shows that the system cannot demonstrate its own consistency.\nEmploying a diagonal argument, Gödel's incompleteness theorems were the first of several closely related theorems on the limitations of formal systems. They were followed by Tarski's undefinability theorem on the formal undefinability of truth, Church's proof that Hilbert's Entscheidungsproblem is unsolvable, and Turing's theorem that there is no algorithm to solve the halting problem.\nFormal systems: completeness, consistency, and effective axiomatization"]], "corpus": "wikipedia"}