Can Large Language Models Derive New Knowledge? A Dynamic Benchmark for Biological Knowledge Discovery
DBench-Bio is a dynamic, automated benchmark designed to evaluate Large Language Models' capacity for biological knowledge discovery. The benchmark addresses data contamination issues in static datasets by employing a three-stage pipeline that acquires authoritative paper abstracts, synthesizes scientific hypothesis questions, and filters quality through relevance, clarity, and centrality metrics. Covering 12 biomedical sub-domains with monthly updates, DBench-Bio reveals current limitations in AI systems' ability to discover truly new knowledge.
arXiv:2603.03322v1 Announce Type: cross
Abstract: Recent advancements in Large Language Model (LLM) agents have demonstrated remarkable potential in automatic knowledge discovery. However, rigorously evaluating an AI's capacity for knowledge discovery remains a critical challenge. Existing benchmarks predominantly rely on static datasets, leading to inevitable data contamination where models have likely seen the evaluation knowledge during training. Furthermore, the rapid release cycles of modern LLMs render static benchmarks quickly outdated, failing to assess the ability to discover truly new knowledge. To address these limitations, we propose DBench-Bio, a dynamic and fully automated benchmark designed to evaluate AI's biological knowledge discovery ability. DBench-Bio employs a three-stage pipeline: (1) data acquisition of rigorous, authoritative paper abstracts; (2) QA extraction utilizing LLMs to synthesize scientific hypothesis questions and corresponding discovery answers; and (3) QA filter to ensure quality based on relevance, clarity, and centrality. We instantiate this pipeline to construct a monthly-updated benchmark covering 12 biomedical sub-domains. Extensive evaluations of SOTA models reveal current limitations in discovering new knowledge. Our work provides the first dynamic, automatic framework for assessing the new knowledge discovery capabilities of AI systems, establishing a living, evolving resource for AI research community to catalyze the development of knowledge discovery.