Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with
  100+ NLP Researchers
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.
66df43c2cf19a8918418b1c1
Chenglei Si
admin_assigned
2024-09-13T07:22:12.135Z
6144c0aa8a3e54bb3ecef8ea
https://cdn-avatars.huggingface.co/v1/production/uploads/1631895645405-noauth.jpeg
Chenglei Si
user
CLS
66df43c2cf19a8918418b1c2
Diyi Yang
66df43c2cf19a8918418b1c3
Tatsunori Hashimoto
Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with
  100+ NLP Researchers
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.
LLM-generated research ideas are perceived as more novel than those from human experts but are deemed slightly less feasible, based on blind evaluations by NLP researchers.
large language models LLMs research agents NLP researchers research ideation self-evaluation diversity in generation feasibility novelty