Tal Ifargan, Lukas Hafner, Maor Kern, Ori Alcalay, Roy Kishony
{"title":"从数据到可由人类验证的研究论文的自主 LLM 驱动型研究","authors":"Tal Ifargan, Lukas Hafner, Maor Kern, Ori Alcalay, Roy Kishony","doi":"arxiv-2404.17605","DOIUrl":null,"url":null,"abstract":"As AI promises to accelerate scientific discovery, it remains unclear whether\nfully AI-driven research is possible and whether it can adhere to key\nscientific values, such as transparency, traceability and verifiability.\nMimicking human scientific practices, we built data-to-paper, an automation\nplatform that guides interacting LLM agents through a complete stepwise\nresearch process, while programmatically back-tracing information flow and\nallowing human oversight and interactions. In autopilot mode, provided with\nannotated data alone, data-to-paper raised hypotheses, designed research plans,\nwrote and debugged analysis codes, generated and interpreted results, and\ncreated complete and information-traceable research papers. Even though\nresearch novelty was relatively limited, the process demonstrated autonomous\ngeneration of de novo quantitative insights from data. For simple research\ngoals, a fully-autonomous cycle can create manuscripts which recapitulate\npeer-reviewed publications without major errors in about 80-90%, yet as goal\ncomplexity increases, human co-piloting becomes critical for assuring accuracy.\nBeyond the process itself, created manuscripts too are inherently verifiable,\nas information-tracing allows to programmatically chain results, methods and\ndata. Our work thereby demonstrates a potential for AI-driven acceleration of\nscientific discovery while enhancing, rather than jeopardizing, traceability,\ntransparency and verifiability.","PeriodicalId":501219,"journal":{"name":"arXiv - QuanBio - Other Quantitative Biology","volume":"4 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Autonomous LLM-driven research from data to human-verifiable research papers\",\"authors\":\"Tal Ifargan, Lukas Hafner, Maor Kern, Ori Alcalay, Roy Kishony\",\"doi\":\"arxiv-2404.17605\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"As AI promises to accelerate scientific discovery, it remains unclear whether\\nfully AI-driven research is possible and whether it can adhere to key\\nscientific values, such as transparency, traceability and verifiability.\\nMimicking human scientific practices, we built data-to-paper, an automation\\nplatform that guides interacting LLM agents through a complete stepwise\\nresearch process, while programmatically back-tracing information flow and\\nallowing human oversight and interactions. In autopilot mode, provided with\\nannotated data alone, data-to-paper raised hypotheses, designed research plans,\\nwrote and debugged analysis codes, generated and interpreted results, and\\ncreated complete and information-traceable research papers. Even though\\nresearch novelty was relatively limited, the process demonstrated autonomous\\ngeneration of de novo quantitative insights from data. For simple research\\ngoals, a fully-autonomous cycle can create manuscripts which recapitulate\\npeer-reviewed publications without major errors in about 80-90%, yet as goal\\ncomplexity increases, human co-piloting becomes critical for assuring accuracy.\\nBeyond the process itself, created manuscripts too are inherently verifiable,\\nas information-tracing allows to programmatically chain results, methods and\\ndata. Our work thereby demonstrates a potential for AI-driven acceleration of\\nscientific discovery while enhancing, rather than jeopardizing, traceability,\\ntransparency and verifiability.\",\"PeriodicalId\":501219,\"journal\":{\"name\":\"arXiv - QuanBio - Other Quantitative Biology\",\"volume\":\"4 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-04-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - QuanBio - Other Quantitative Biology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2404.17605\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuanBio - Other Quantitative Biology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2404.17605","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Autonomous LLM-driven research from data to human-verifiable research papers
As AI promises to accelerate scientific discovery, it remains unclear whether
fully AI-driven research is possible and whether it can adhere to key
scientific values, such as transparency, traceability and verifiability.
Mimicking human scientific practices, we built data-to-paper, an automation
platform that guides interacting LLM agents through a complete stepwise
research process, while programmatically back-tracing information flow and
allowing human oversight and interactions. In autopilot mode, provided with
annotated data alone, data-to-paper raised hypotheses, designed research plans,
wrote and debugged analysis codes, generated and interpreted results, and
created complete and information-traceable research papers. Even though
research novelty was relatively limited, the process demonstrated autonomous
generation of de novo quantitative insights from data. For simple research
goals, a fully-autonomous cycle can create manuscripts which recapitulate
peer-reviewed publications without major errors in about 80-90%, yet as goal
complexity increases, human co-piloting becomes critical for assuring accuracy.
Beyond the process itself, created manuscripts too are inherently verifiable,
as information-tracing allows to programmatically chain results, methods and
data. Our work thereby demonstrates a potential for AI-driven acceleration of
scientific discovery while enhancing, rather than jeopardizing, traceability,
transparency and verifiability.