Measuring the Gap Between Human and LLM Research Ideas
Summary
This research paper introduces a framework to measure the distributional gap between human-generated and LLM-generated research ideas, finding that LLM ideas are concentrated around specific opportunity patterns and synthesis methods, while human ideas are more diverse.
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Paper page - Measuring the Gap Between Human and LLM Research Ideas
Source: https://huggingface.co/papers/2607.01233 Published on Jul 1
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Submitted byhttps://huggingface.co/ziyuuc
Ziyuon Jul 6
Abstract
Large language models generate research ideas that cluster around specific opportunity patterns and paradigms, diverging systematically from the broader and more diverse distributions found in human research papers.
LLMs are increasingly used to brainstorm research ideas, but existing evaluations mostly judge individual ideas by novelty, feasibility, or expert preference. We instead ask: how far are current LLM-generated ideas from human researchers? To characterize this gap, we build a large-scale evaluation framework for ideation from high-qualityhuman research papers. For each paper, we reverse-engineer a small set of closely related prior works that likely inspired its core idea. LLMs are then prompted to generate a new idea from the set of paper titles and summaries. We introduce a two-axis research-taste taxonomy to profile each idea by its opportunity pattern and research paradigm, and use it to quantify the divergence between human and LLM ideas. Across idea sets generated by different LLMs, we observe a consistent distributional gap: LLM ideas are disproportionately concentrated around bridge-like opportunities and synthesis methods, whereas the human paper reference distribution spreads more broadly across ways of framing gaps and constructing contributions. This result suggests that strong LLMs can produce a range of reasonable ideas, but that range remains narrower than, and systematically shifted relative to, human research taste.
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