Skip to main content We gratefully acknowledge support from the Simons Foundation, member institutions , and all contributors. Donate > cs > arXiv:2409.04109 Help | Advanced Search All fields Title Author Abstract Comments Journal reference ACM classification MSC classification Report number arXiv identifier DOI ORCID arXiv author ID Help pages Full text Search open search GO open navigation menu quick links Login Help Pages About --> Computer Science > Computation and Language arXiv:2409.04109 (cs) [Submitted on 6 Sep 2024] Title: Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers Authors: Chenglei Si , Diyi Yang , Tatsunori Hashimoto View a PDF of the paper titled Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers, by Chenglei Si and 2 other authors View PDF HTML (experimental) Abstract: Recent advancements in large language models (LLMs) have sparked optimism about their potential to accelerate scientific discovery, with a growing number of works proposing research agents that autonomously generate and validate new ideas. Despite this, no evaluations have shown that LLM systems can take the very first step of producing novel, expert-level ideas, let alone perform the entire research process. We address this by establishing an experimental design that evaluates research idea generation while controlling for confounders and performs the first head-to-head comparison between expert NLP researchers and an LLM ideation agent. By recruiting over 100 NLP researchers to write novel ideas and blind reviews of both LLM and human ideas, we obtain the first statistically significant conclusion on current LLM capabilities for research ideation: we find LLM-generated ideas are judged as more novel (p < 0.05) than human expert ideas while being judged slightly weaker on feasibility. Studying our agent baselines closely, we identify open problems in building and evaluating research agents, including failures of LLM self-evaluation and their lack of diversity in generation. Finally, we acknowledge that human judgements of novelty can be difficult, even by experts, and propose an end-to-end study design which recruits researchers to execute these ideas into full projects, enabling us to study whether these novelty and feasibility judgements result in meaningful differences in research outcome. Comments: main paper is 20 pages Subjects: Computation and Language (cs.CL) ; Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG) Cite as: arXiv:2409.04109 [cs.CL] (or arXiv:2409.04109v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2409.04109 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Chenglei Si [ view email ] [v1] Fri, 6 Sep 2024 08:25:03 UTC (382 KB) Full-text links: Access Paper: View a PDF of the paper titled Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers, by Chenglei Si and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CL < prev | next > new | recent | 2024-09 Change to browse by: cs cs.AI cs.CY cs.HC cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation &times; loading... 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