arXiv:2607.01233v1 Announce Type: new Abstract: 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-quality human 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
The proliferation of Large Language Models (LLMs) for ideation necessitates a robust framework to understand their creative capabilities compared to human researchers.
Understanding the 'idea gap' between humans and LLMs is crucial for strategically deploying AI in research and innovation, impacting R&D investment and human capital allocation.
This research introduces a novel, large-scale evaluation method for assessing LLM ideation, moving beyond simple novelty or feasibility metrics.
- · AI research labs
- · Companies investing in R&D
- · AI agents developers
- · Businesses solely relying on human ideation without AI augmentation
- · Legacy research methodologies
Increased understanding of LLMs' strengths and weaknesses in generating novel research ideas.
Development of refined LLM architectures and prompting techniques specifically geared toward closing identified 'idea gaps'.
Potential for LLMs to eventually surpass human ideation in specific and then broader scientific domains, leading to accelerated discovery.
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Read at arXiv cs.CL