title: Can Large Language Models Unlock Novel Scientific Research Ideas?
summary: "An idea is nothing more nor less than a new combination of old elements"
  (Young, J.W.). The widespread adoption of Large Language Models (LLMs) and
  publicly available ChatGPT have marked a significant turning point in the
  integration of Artificial Intelligence (AI) into people's everyday lives. This
  study explores the capability of LLMs in generating novel research ideas based
  on information from research papers. We conduct a thorough examination of 4
  LLMs in five domains (e.g., Chemistry, Computer, Economics, Medical, and
  Physics). We found that the future research ideas generated by Claude-2 and
  GPT-4 are more aligned with the author's perspective than GPT-3.5 and Gemini.
  We also found that Claude-2 generates more diverse future research ideas than
  GPT-4, GPT-3.5, and Gemini 1.0. We further performed a human evaluation of the
  novelty, relevancy, and feasibility of the generated future research ideas.
  This investigation offers insights into the evolving role of LLMs in idea
  generation, highlighting both its capability and limitations. Our work
  contributes to the ongoing efforts in evaluating and utilizing language models
  for generating future research ideas. We make our datasets and codes publicly
  available.
paper_authors._id: 66e264a631fa3bf05b28a448
paper_authors.name: Sandeep Kumar
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-09-12T07:11:59.496Z
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paper_authors.fullname: Kumar
paper_authors.type: user
paper_authors.user: sandeep123
paper_authors._id: 66e264a631fa3bf05b28a449
paper_authors.name: Tirthankar Ghosal
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-12T08:54:53.207Z
paper_authors._id: 65ae8915c92e09d5cd6a402f
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paper_authors.fullname: Tirthankar Ghosal
paper_authors.type: user
paper_authors.user: TirthankarSlg
paper_authors._id: 66e264a631fa3bf05b28a44a
paper_authors.name: Vinayak Goyal
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-09-12T09:06:58.681Z
paper_authors._id: 661164d8db728ea9676dbb8e
paper_authors.avatarUrl: /avatars/35710c69ef0070a2730a21928250dec6.svg
paper_authors.fullname: Vinayak Goyal
paper_authors.type: user
paper_authors.user: Vinayak-goyal
paper_authors._id: 66e264a631fa3bf05b28a44b
paper_authors.name: Asif Ekbal
paper_title: Can Large Language Models Unlock Novel Scientific Research Ideas?
paper_summary: "An idea is nothing more nor less than a new combination of old elements"
  (Young, J.W.). The widespread adoption of Large Language Models (LLMs) and
  publicly available ChatGPT have marked a significant turning point in the
  integration of Artificial Intelligence (AI) into people's everyday lives. This
  study explores the capability of LLMs in generating novel research ideas based
  on information from research papers. We conduct a thorough examination of 4
  LLMs in five domains (e.g., Chemistry, Computer, Economics, Medical, and
  Physics). We found that the future research ideas generated by Claude-2 and
  GPT-4 are more aligned with the author's perspective than GPT-3.5 and Gemini.
  We also found that Claude-2 generates more diverse future research ideas than
  GPT-4, GPT-3.5, and Gemini 1.0. We further performed a human evaluation of the
  novelty, relevancy, and feasibility of the generated future research ideas.
  This investigation offers insights into the evolving role of LLMs in idea
  generation, highlighting both its capability and limitations. Our work
  contributes to the ongoing efforts in evaluating and utilizing language models
  for generating future research ideas. We make our datasets and codes publicly
  available.
paper_ai_summary: LLMs, particularly Claude-2, generate more novel and diverse research ideas compared to other models, as assessed through human evaluation across multiple domains.
paper_ai_keywords: Large Language Models ChatGPT Artificial Intelligence future research ideas Claude-2 GPT-4 GPT-3.5 Gemini human evaluation novelty relevance feasibility