ResearchBench: Benchmarking LLMs in Scientific Discovery via Inspiration-Based Task Decomposition

Understand

Large language models (LLMs) have shown potential in assisting scientific research, yet their ability to discover high-quality research hypotheses remains unexamined due to the lack of a dedicated benchmark.

• To address this gap, we introduce the first large-scale benchmark for evaluating LLMs on a sufficient set of scientific discovery sub-tasks-inspiration retrieval, hypothesis composition, and hypothesis ranking-where sufficient means that perfectly solving these sub-tasks perfectly solves the overall discovery task.
• We develop an automated LLM-based framework that extracts critical components-research questions, background surveys, inspirations, and hypotheses-from papers across 12 disciplines, with expert validation confirming its accuracy.
• To prevent data contamination, we focus exclusively on publications from 2024 onward, ensuring minimal overlap with LLM pretraining data; our automated framework further enables automatic extraction of even more recent papers as LLM pretraining cutoffs advance, supporting scalable and contamination-free automatic renewal of this discovery benchmark.