Evaluating Large Language Models in Scientific Discovery (arxiv.org)

A recent study introduces a novel benchmark for evaluating large language models (LLMs) in the context of scientific discovery, highlighting their limitations in previous assessments that often focus on isolated knowledge rather than the comprehensive processes involved in research. This scenario-grounded benchmark assesses LLMs across various scientific domains—biology, chemistry, materials, and physics—by requiring them to tackle research projects identified by domain experts. The evaluation occurs at two levels: first, measuring question-level accuracy linked to specific research scenarios, and second, assessing project-level performance, which involves formulating testable hypotheses, designing experiments, and interpreting results. This framework reveals a consistent performance gap when LLMs are compared to traditional science benchmarks, illustrating diminishing returns from increasing model sizes and revealing shared weaknesses across models from different providers. Notably, despite the identified limitations, LLMs demonstrate potential in diverse scientific discovery endeavors, particularly in scenarios where structured scenario scores are low. This SDE framework not only provides a reproducible method for evaluating LLMs in research contexts but also lays the groundwork for developing their capabilities towards achieving greater effectiveness in scientific innovation.