Can Large Language Models Derive New Knowledge? A Dynamic Benchmark for Biological Knowledge Discovery

TL;DR

This paper introduces DBench-Bio, a dynamic, automated benchmark for evaluating AI's ability to discover new biological knowledge, addressing limitations of static datasets and outdated evaluation methods.

Contribution

We propose a novel, fully automated, monthly-updated benchmark that assesses AI's capacity for biological knowledge discovery using a three-stage pipeline.

Findings

Current SOTA models show limitations in discovering new knowledge.

DBench-Bio provides a living resource for ongoing evaluation.

The framework can be adapted to other scientific domains.

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 Furthermore, the rapid release cycles of modern LLMs render static benchmarks quickly outdated, failing to assess the ability to discover truly new knowledge.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.