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 {"tstamp": 1731351595.6967, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "f1df02b7e5a44425a7c95f7aa4d5d870", "0_model_name": "BAAI/bge-large-en-v1.5", "0_prompt": "I want to explore how the integration of comprehensive research paper content, beyond titles and abstracts, affects the performance of retrieval models in scientific literature search. Query: What if retrieval systems could effectively utilize the full context of research papers, including inline-citation questions, to enhance the accuracy and relevance of search results in the domain of machine learning and natural language processing? How might this approach transform the capabilities of current retrieval models and reranking strategies, such as BM25 and dense retrievers, in addressing complex literature search queries?", "0_output": [["I want to explore how the integration of comprehensive research paper content, beyond titles and abstracts, affects the performance of retrieval models in scientific literature search. Query: What if retrieval systems could effectively utilize the full context of research papers, including inline-citation questions, to enhance the accuracy and relevance of search results in the domain of machine learning and natural language processing? How might this approach transform the capabilities of current retrieval models and reranking strategies, such as BM25 and dense retrievers, in addressing complex literature search queries?", "Title: The Inefficiency of Language Models in Scholarly Retrieval: An Experimental Walk-through\n\nAbstract: Language models are increasingly becoming popular in AI-powered scientific IR systems. This paper evaluates popular scientific language models in handling (i) short-query texts and (ii) textual neighbors. Our experiments showcase the inability to retrieve relevant documents for a short-query text even under the most relaxed conditions. Additionally, we leverage textual neighbors, generated by small perturbations to the original text, to demonstrate that not all perturbations lead to close neighbors in the embedding space. Further, an exhaustive categorization yields several classes of orthographically and semantically related, partially related, and completely unrelated neighbors. Retrieval performance turns out to be more influenced by the surface form rather than the semantics of the text."]], "0_corpus": "arxiv", "1_conv_id": "21f3ebcacbb64314a7b42659fe081cf5", "1_model_name": "Salesforce/SFR-Embedding-2_R", "1_prompt": "I want to explore how the integration of comprehensive research paper content, beyond titles and abstracts, affects the performance of retrieval models in scientific literature search. Query: What if retrieval systems could effectively utilize the full context of research papers, including inline-citation questions, to enhance the accuracy and relevance of search results in the domain of machine learning and natural language processing? How might this approach transform the capabilities of current retrieval models and reranking strategies, such as BM25 and dense retrievers, in addressing complex literature search queries?", "1_output": [["I want to explore how the integration of comprehensive research paper content, beyond titles and abstracts, affects the performance of retrieval models in scientific literature search. Query: What if retrieval systems could effectively utilize the full context of research papers, including inline-citation questions, to enhance the accuracy and relevance of search results in the domain of machine learning and natural language processing? How might this approach transform the capabilities of current retrieval models and reranking strategies, such as BM25 and dense retrievers, in addressing complex literature search queries?", "Title: DocReLM: Mastering Document Retrieval with Language Model\n\nAbstract: With over 200 million published academic documents and millions of new documents being written each year, academic researchers face the challenge of searching for information within this vast corpus. However, existing retrieval systems struggle to understand the semantics and domain knowledge present in academic papers. In this work, we demonstrate that by utilizing large language models, a document retrieval system can achieve advanced semantic understanding capabilities, significantly outperforming existing systems. Our approach involves training the retriever and reranker using domain-specific data generated by large language models. Additionally, we utilize large language models to identify candidates from the references of retrieved papers to further enhance the performance. We use a test set annotated by academic researchers in the fields of quantum physics and computer vision to evaluate our system's performance. The results show that DocReLM achieves a Top 10 accuracy of 44.12% in computer vision, compared to Google Scholar's 15.69%, and an increase to 36.21% in quantum physics, while that of Google Scholar is 12.96%."]], "1_corpus": "arxiv"}
 {"tstamp": 1731355504.7459, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "175d4b58415d4a638e0832addb57d7df", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": "Which test was devised to determine whether robots can think?", "0_output": [["Which test was devised to determine whether robots can think?", "Cognitive test\n\nCognitive tests are assessments of the cognitive capabilities of humans and other animals. Tests administered to humans include various forms of IQ tests; those administered to animals include the mirror test (a test of visual self-awareness) and the T maze test (which tests learning ability). Such testing is used in psychology and psychometrics, as well as other fields studying human and animal intelligence."]], "0_corpus": "wikipedia", "1_conv_id": "84c667f73b3d4e01ab16fa4d1abc517f", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": "Which test was devised to determine whether robots can think?", "1_output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic)."]], "1_corpus": "wikipedia"}
 {"tstamp": 1731355664.2347, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "2519dc3ba6394fbba81907539ba647a5", "0_model_name": "BM25", "0_prompt": "What's the purpose of `if __name__ == '__main__'`?", "0_output": [["What's the purpose of `if __name__ == '__main__'`?", "Stackoverflow Stackexchange\n\nQ: What is the purpose of __name__? What does __name__ do? I have only seen it paired with __main__ and nothing else.\nI know that the classic if __name__ == __main__: defines the behavior when acting as a package vs running as stand-alone.\nHowever what other usages are there for __name__?\n\nA: __name__ is \"__main__\" if you're executing the script directly. If you're importing a module, __name__ is the name of the module.\nfoo.py:\nprint(__name__)\n\nbar.py \nimport foo\n\nRun the scripts:\n$ python foo.py\n__main__\n$ python bar.py \nfoo\n\n"]], "0_corpus": "stackexchange", "1_conv_id": "f136d256b0de4ddd8485e144a67bdb6f", "1_model_name": "intfloat/e5-mistral-7b-instruct", "1_prompt": "What's the purpose of `if __name__ == '__main__'`?", "1_output": [["What's the purpose of `if __name__ == '__main__'`?", "Stackoverflow Stackexchange\n\nQ: What does `if name == \"__main__\"` mean in Python? \nPossible Duplicate:\nWhat does <if name==“main”:> do? \n\nI have wrote scripts in Python for quite a while now and I study more of Python as I need it. When reading other people's code I meet if name == \"__main__\": construct quite often. \nWhat is it good for?\n\nA: Basically, \nThere's a distinction between the \"main\" script file and external files which were imported or referenced in another way. If the script is the \"main\" script then the special variable __name__ will equal \"__main__\".\nYou can use this to protect the \"execution\" code from the classes and variables the script has. This enables you to import a script and use classes and variables without actually running that script's main code if it has any.\nSee also: What does if name == “main”: do?\n"]], "1_corpus": "stackexchange"}
+{"tstamp": 1731363641.1335, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "d8b77ca5c9b9471aa39afd3afe03e5a9", "0_model_name": "BAAI/bge-large-en-v1.5", "0_prompt": "when was the first commercial cell phone released", "0_output": [["when was the first commercial cell phone released", "Mobile phone\n\nHistory A handheld mobile radio telephone service was envisioned in the early stages of radio engineering. In 1917, Finnish inventor Eric Tigerstedt filed a patent for a \"pocket-size folding telephone with a very thin carbon microphone\". Early predecessors of cellular phones included analog radio communications from ships and trains. The race to create truly portable telephone devices began after World War II, with developments taking place in many countries. The advances in mobile telephony have been traced in successive \"generations\", starting with the early zeroth-generation (0G) services, such as Bell System's Mobile Telephone Service and its successor, the Improved Mobile Telephone Service. These 0G systems were not cellular, supported a few simultaneous calls, and were very expensive.\nThe first handheld cellular mobile phone was demonstrated by John F. Mitchell and Martin Cooper of Motorola in 1973, using a handset weighing . The first commercial automated cellular network (1G) analog was launched in Japan by Nippon Telegraph and Telephone in 1979. This was followed in 1981 by the simultaneous launch of the Nordic Mobile Telephone (NMT) system in Denmark, Finland, Norway, and Sweden. Several other countries then followed in the early to mid-1980s. These first-generation (1G) systems could support far more simultaneous calls but still used analog cellular technology. In 1983, the DynaTAC 8000x was the first commercially available handheld mobile phone."]], "0_corpus": "wikipedia", "1_conv_id": "ffeea8d2606e478a8870377c852edfe8", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "when was the first commercial cell phone released", "1_output": [["when was the first commercial cell phone released", "History of mobile phones\n\nIn 1959, a private telephone company in Brewster, Kansas, US, the S&T Telephone Company, (still in business today) with the use of Motorola Radio Telephone equipment and a private tower facility, offered to the public mobile telephone services in that local area of NW Kansas. This system was a direct dial up service through their local switchboard, and was installed in many private vehicles including grain combines, trucks, and automobiles. For some as yet unknown reason, the system, after being placed online and operated for a very brief time period, was shut down. The management of the company was immediately changed, and the fully operable system and related equipment was immediately dismantled in early 1960, not to be seen again.\nIn 1966, Bulgaria presented the pocket mobile automatic phone RAT-0,5 combined with a base station RATZ-10 (RATC-10) on Interorgtechnika-66 international exhibition. One base station, connected to one telephone wire line, could serve up to six customers.\nOne of the first successful public commercial mobile phone networks was the ARP network in Finland, launched in 1971. Posthumously, ARP is sometimes viewed as a zero generation (0G) cellular network, being slightly above previous proprietary and limited coverage networks.\nHandheld mobile phone\nPrior to 1973, mobile telephony was limited to phones installed in cars and other vehicles. The first portable cellular phone commercially available for use on a cellular network was developed by E.F. Johnson and Millicom, Inc. It was introduced by Millicom subsidiary Comvik in Sweden in September 1981."]], "1_corpus": "wikipedia"}