Scheduled Commit

data/retrieval_battle-2cea24b1-39e9-480a-ba22-c617ea05c1fe.jsonl

@@ -54,3 +54,4 @@
 {"tstamp": 1724147306.6021, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "289a6fa2c341488b878cbdd72a776881", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "is asgard a real planet?", "0_output": [["is asgard a real planet?", "Scifi Stackexchange\n\nQ: Why is it called Middle-earth? The planet is called Arda.\nSo why do they call it \"Middle-earth\" if there is no planet called Earth to have a middle of?\n\nA: \"Middle-earth\" is a modern version of the word \"Midgard\" which in Norse mythology is the world that humans live on (there are eight others). Tolkien was known to derive some of the mythology of Middle-earth from Norse mythology (including parts of the Ainulindalë, the creation scene at the beginning of the Silmarillion).\n"]], "0_corpus": "stackexchange", "1_conv_id": "db25d91c18d2409ab674107856390073", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": "is asgard a real planet?", "1_output": [["is asgard a real planet?", "Scifi Stackexchange\n\nQ: What’s the official in-universe classification of Thor and other Asgardians in the MCU? What’s the official in-universe classification of Asgardians in the MCU?\nBy official, I mean what category does S.H.I.E.L.D. put them into, i.e. “Alien,” “Human” or do they just designate them as “Gods?”\n\nA: S.H.I.E.L.D. likely classifies Asgardians as aliens, judging by this dialogue from an early episode of Agents of S.H.I.E.L.D..\n\nSKYE: So, Asgardians are aliens from another planet that visited us thousands of years ago?\nAGENT COULSON: Or more.\nSKYE: And because we couldn't understand aliens, we thought they were gods?\nAGENT COULSON: That's where our Norse mythology comes from.\nSKYE: [sighs] That's too crazy. Do you think other deities are aliens, too? Vishnu for sure, right?\nAgents of S.H.I.E.L.D. - S01E08 - \"The Well\"\n\nOdin probably would've agreed with this classification, since he clearly stated that MCU Asgardians aren't gods.\n\nLOKI: I went down to Midgard to rule the people of Earth as a benevolent god. Just like you.\nODIN: We are not gods. We are born, we live, we die. Just as humans do.\nLOKI: Give or take 5000 years.\nThor: The Dark World (2013)\n\n"]], "1_corpus": "stackexchange"}
 {"tstamp": 1724147323.7292, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "b1622a49d84d41de81be59458d957680", "0_model_name": "BM25", "0_prompt": "How to combine GPT-2 and BERT for better question generation and answering?", "0_output": [["How to combine GPT-2 and BERT for better question generation and answering?", "Title: Learning to Answer by Learning to Ask: Getting the Best of GPT-2 and BERT Worlds\n\nAbstract: Automatic question generation aims at the generation of questions from a context, with the corresponding answers being sub-spans of the given passage. Whereas, most of the methods mostly rely on heuristic rules to generate questions, more recently also neural network approaches have been proposed. In this work, we propose a variant of the self-attention Transformer network architectures model to generate meaningful and diverse questions. To this end, we propose an easy to use model consisting of the conjunction of the Transformer decoder GPT-2 model with Transformer encoder BERT for the downstream task for question answering. The model is trained in an end-to-end fashion, where the language model is trained to produce a question-answer-aware input representation that facilitates to generate an answer focused question. Our result of neural question generation from text on the SQuAD 1.1 dataset suggests that our method can produce semantically correct and diverse questions. Additionally, we assessed the performance of our proposed method for the downstream task of question answering. The analysis shows that our proposed generation & answering collaboration framework relatively improves both tasks and is particularly powerful in the semi-supervised setup. The results further suggest a robust and comparably lean pipeline facilitating question generation in the small-data regime."]], "0_corpus": "arxiv", "1_conv_id": "a1b61891a5fc41888a6df192888fa1c6", "1_model_name": "text-embedding-3-large", "1_prompt": "How to combine GPT-2 and BERT for better question generation and answering?", "1_output": [["How to combine GPT-2 and BERT for better question generation and answering?", "Title: Learning to Answer by Learning to Ask: Getting the Best of GPT-2 and BERT Worlds\n\nAbstract: Automatic question generation aims at the generation of questions from a context, with the corresponding answers being sub-spans of the given passage. Whereas, most of the methods mostly rely on heuristic rules to generate questions, more recently also neural network approaches have been proposed. In this work, we propose a variant of the self-attention Transformer network architectures model to generate meaningful and diverse questions. To this end, we propose an easy to use model consisting of the conjunction of the Transformer decoder GPT-2 model with Transformer encoder BERT for the downstream task for question answering. The model is trained in an end-to-end fashion, where the language model is trained to produce a question-answer-aware input representation that facilitates to generate an answer focused question. Our result of neural question generation from text on the SQuAD 1.1 dataset suggests that our method can produce semantically correct and diverse questions. Additionally, we assessed the performance of our proposed method for the downstream task of question answering. The analysis shows that our proposed generation & answering collaboration framework relatively improves both tasks and is particularly powerful in the semi-supervised setup. The results further suggest a robust and comparably lean pipeline facilitating question generation in the small-data regime."]], "1_corpus": "arxiv"}
 {"tstamp": 1724147373.7737, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "ee7d7009cb09443086721d839839d30b", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_prompt": "Why did the machines in The Matrix keep humans around?", "0_output": [["Why did the machines in The Matrix keep humans around?", "Scifi Stackexchange\n\nQ: Why use full brained humans as batteries? \nPossible Duplicate:\nIs the basic premise of humans as a power source in The Matrix reasonable?\nWhy Did the Machines Even Bother With a Matrix? \n\nThe machines in The Matrix series use humans as batteries. That's OK. But why did the machines not alter their livestock such that the matrix itself would be unnecessary?  \nConsider the recently proposed headless chicken farm. Something similar for the human batteries would seem to be an ideal solution for logical machines. No longer would they need to spend resources maintaining a virtual reality for their batteries. The energy, physical hardware and programs running the matrix could then be put to who knows what other uses.\n\nA: The explanation for the use of human brains would seem to be the fact that our complex thoughts lead to higher level of brain activity, thus resulting in exponentially more synapses firing (i.e. more electrical activity to harvest).\nThat makes human brains far more efficient than the brains of animals, even with the overhead of maintaining the matrix.  Plus, that movie would have really sucked... dumb cows and chickens revolt against the machines!  ;)\n"]], "0_corpus": "stackexchange", "1_conv_id": "b1e3a8689e72427ea133d86987f91a26", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "Why did the machines in The Matrix keep humans around?", "1_output": [["Why did the machines in The Matrix keep humans around?", "Scifi Stackexchange\n\nQ: Do the Machines in \"The Matrix\" Adhere to the Three Laws of Robotics? In the \"real world\" of the Matrix, do the machines follow Asimov's Three Rules? Were they even programmed with them?\nEDIT: I realize that killing people goes against the 1st law, but by keeping people in the Matrix (which the machines view as a symbiosis) rather than simply killing off these \"annoying\" creatures, I wonder if the killings can be seen as \"preserving the greater good\" or something along those lines; and thus justify violating the laws.\n\nA: \nA robot may not injure a human being or, through inaction, allow a\n  human being to come to harm.\nA robot must obey the orders given to it by human beings, except where\n  such orders would conflict with the First Law.\nA robot must protect its own existence as long as such protection does\n  not conflict with the First or Second Laws.\n\nNo, no, and no.\n"]], "1_corpus": "stackexchange"}
+{"tstamp": 1724150191.1245, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "14abff6b4bf4468abd5725f738f67892", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "Research on structured dropout as a way to improve transformer models during both training and inference.", "0_output": [["Research on structured dropout as a way to improve transformer models during both training and inference.", "Title: Reducing Transformer Depth on Demand with Structured Dropout\n\nAbstract: Overparameterized transformer networks have obtained state of the art results in various natural language processing tasks, such as machine translation, language modeling, and question answering. These models contain hundreds of millions of parameters, necessitating a large amount of computation and making them prone to overfitting. In this work, we explore LayerDrop, a form of structured dropout, which has a regularization effect during training and allows for efficient pruning at inference time. In particular, we show that it is possible to select sub-networks of any depth from one large network without having to finetune them and with limited impact on performance. We demonstrate the effectiveness of our approach by improving the state of the art on machine translation, language modeling, summarization, question answering, and language understanding benchmarks. Moreover, we show that our approach leads to small BERT-like models of higher quality compared to training from scratch or using distillation."]], "0_corpus": "arxiv", "1_conv_id": "d0bfba6aabaf459ab1bfdc30a214e502", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "Research on structured dropout as a way to improve transformer models during both training and inference.", "1_output": [["Research on structured dropout as a way to improve transformer models during both training and inference.", "Title: Reducing Transformer Depth on Demand with Structured Dropout\n\nAbstract: Overparameterized transformer networks have obtained state of the art results in various natural language processing tasks, such as machine translation, language modeling, and question answering. These models contain hundreds of millions of parameters, necessitating a large amount of computation and making them prone to overfitting. In this work, we explore LayerDrop, a form of structured dropout, which has a regularization effect during training and allows for efficient pruning at inference time. In particular, we show that it is possible to select sub-networks of any depth from one large network without having to finetune them and with limited impact on performance. We demonstrate the effectiveness of our approach by improving the state of the art on machine translation, language modeling, summarization, question answering, and language understanding benchmarks. Moreover, we show that our approach leads to small BERT-like models of higher quality compared to training from scratch or using distillation."]], "1_corpus": "arxiv"}

data/retrieval_individual-2cea24b1-39e9-480a-ba22-c617ea05c1fe.jsonl

@@ -181,3 +181,5 @@
 {"tstamp": 1724147316.5565, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1724147315.7701, "finish": 1724147316.5565, "ip": "", "conv_id": "a1b61891a5fc41888a6df192888fa1c6", "model_name": "text-embedding-3-large", "prompt": "How to combine GPT-2 and BERT for better question generation and answering?", "output": [["How to combine GPT-2 and BERT for better question generation and answering?", "Title: Learning to Answer by Learning to Ask: Getting the Best of GPT-2 and BERT Worlds\n\nAbstract: Automatic question generation aims at the generation of questions from a context, with the corresponding answers being sub-spans of the given passage. Whereas, most of the methods mostly rely on heuristic rules to generate questions, more recently also neural network approaches have been proposed. In this work, we propose a variant of the self-attention Transformer network architectures model to generate meaningful and diverse questions. To this end, we propose an easy to use model consisting of the conjunction of the Transformer decoder GPT-2 model with Transformer encoder BERT for the downstream task for question answering. The model is trained in an end-to-end fashion, where the language model is trained to produce a question-answer-aware input representation that facilitates to generate an answer focused question. Our result of neural question generation from text on the SQuAD 1.1 dataset suggests that our method can produce semantically correct and diverse questions. Additionally, we assessed the performance of our proposed method for the downstream task of question answering. The analysis shows that our proposed generation & answering collaboration framework relatively improves both tasks and is particularly powerful in the semi-supervised setup. The results further suggest a robust and comparably lean pipeline facilitating question generation in the small-data regime."]], "corpus": "arxiv"}
 {"tstamp": 1724147350.4213, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1724147348.2289, "finish": 1724147350.4213, "ip": "", "conv_id": "ee7d7009cb09443086721d839839d30b", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": "Why did the machines in The Matrix keep humans around?", "output": [["Why did the machines in The Matrix keep humans around?", "Scifi Stackexchange\n\nQ: Why use full brained humans as batteries? \nPossible Duplicate:\nIs the basic premise of humans as a power source in The Matrix reasonable?\nWhy Did the Machines Even Bother With a Matrix? \n\nThe machines in The Matrix series use humans as batteries. That's OK. But why did the machines not alter their livestock such that the matrix itself would be unnecessary?  \nConsider the recently proposed headless chicken farm. Something similar for the human batteries would seem to be an ideal solution for logical machines. No longer would they need to spend resources maintaining a virtual reality for their batteries. The energy, physical hardware and programs running the matrix could then be put to who knows what other uses.\n\nA: The explanation for the use of human brains would seem to be the fact that our complex thoughts lead to higher level of brain activity, thus resulting in exponentially more synapses firing (i.e. more electrical activity to harvest).\nThat makes human brains far more efficient than the brains of animals, even with the overhead of maintaining the matrix.  Plus, that movie would have really sucked... dumb cows and chickens revolt against the machines!  ;)\n"]], "corpus": "stackexchange"}
 {"tstamp": 1724147350.4213, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1724147348.2289, "finish": 1724147350.4213, "ip": "", "conv_id": "b1e3a8689e72427ea133d86987f91a26", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "Why did the machines in The Matrix keep humans around?", "output": [["Why did the machines in The Matrix keep humans around?", "Scifi Stackexchange\n\nQ: Do the Machines in \"The Matrix\" Adhere to the Three Laws of Robotics? In the \"real world\" of the Matrix, do the machines follow Asimov's Three Rules? Were they even programmed with them?\nEDIT: I realize that killing people goes against the 1st law, but by keeping people in the Matrix (which the machines view as a symbiosis) rather than simply killing off these \"annoying\" creatures, I wonder if the killings can be seen as \"preserving the greater good\" or something along those lines; and thus justify violating the laws.\n\nA: \nA robot may not injure a human being or, through inaction, allow a\n  human being to come to harm.\nA robot must obey the orders given to it by human beings, except where\n  such orders would conflict with the First Law.\nA robot must protect its own existence as long as such protection does\n  not conflict with the First or Second Laws.\n\nNo, no, and no.\n"]], "corpus": "stackexchange"}
+{"tstamp": 1724150176.8905, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1724150174.6886, "finish": 1724150176.8905, "ip": "", "conv_id": "14abff6b4bf4468abd5725f738f67892", "model_name": "GritLM/GritLM-7B", "prompt": "Research on structured dropout as a way to improve transformer models during both training and inference.", "output": [["Research on structured dropout as a way to improve transformer models during both training and inference.", "Title: Reducing Transformer Depth on Demand with Structured Dropout\n\nAbstract: Overparameterized transformer networks have obtained state of the art results in various natural language processing tasks, such as machine translation, language modeling, and question answering. These models contain hundreds of millions of parameters, necessitating a large amount of computation and making them prone to overfitting. In this work, we explore LayerDrop, a form of structured dropout, which has a regularization effect during training and allows for efficient pruning at inference time. In particular, we show that it is possible to select sub-networks of any depth from one large network without having to finetune them and with limited impact on performance. We demonstrate the effectiveness of our approach by improving the state of the art on machine translation, language modeling, summarization, question answering, and language understanding benchmarks. Moreover, we show that our approach leads to small BERT-like models of higher quality compared to training from scratch or using distillation."]], "corpus": "arxiv"}
+{"tstamp": 1724150176.8905, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1724150174.6886, "finish": 1724150176.8905, "ip": "", "conv_id": "d0bfba6aabaf459ab1bfdc30a214e502", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "Research on structured dropout as a way to improve transformer models during both training and inference.", "output": [["Research on structured dropout as a way to improve transformer models during both training and inference.", "Title: Reducing Transformer Depth on Demand with Structured Dropout\n\nAbstract: Overparameterized transformer networks have obtained state of the art results in various natural language processing tasks, such as machine translation, language modeling, and question answering. These models contain hundreds of millions of parameters, necessitating a large amount of computation and making them prone to overfitting. In this work, we explore LayerDrop, a form of structured dropout, which has a regularization effect during training and allows for efficient pruning at inference time. In particular, we show that it is possible to select sub-networks of any depth from one large network without having to finetune them and with limited impact on performance. We demonstrate the effectiveness of our approach by improving the state of the art on machine translation, language modeling, summarization, question answering, and language understanding benchmarks. Moreover, we show that our approach leads to small BERT-like models of higher quality compared to training from scratch or using distillation."]], "corpus": "arxiv"}