EvidenceBench: A Benchmark for Extracting Evidence from Biomedical Papers

Jianyou Wang, Weili Cao, Kaicheng Wang, Xiaoyue Wang, Ashish Dalvi, Gino Prasad, Qishan Liang, Hsuan-lin Her, Mingwang, Qin Yang, Gene W. Yeo, David E Neal, Maxim Khan, Christopher D. Rosin, Ramamohan Paturi, Leon Bergen

Keywords: Biomedical Benchmark, Scientific Information Extraction, Large Language Models, BioNLP

TL;DR: A large biomedical benchmark to evaluate LLMs' evidence-finding ability for scientific hypotheses and an automatic pipeline to create such benchmarks.

Abstract: We study the task of automatically finding evidence relevant to hypotheses in biomedical papers. Finding relevant evidence is an important step when researchers investigate scientific hypotheses. We introduce EvidenceBench to measure models performance on this task, which is created by a novel pipeline that consists of hypothesis generation and sentence-by-sentence annotation of biomedical papers for relevant evidence, completely guided by and faithfully following existing human experts judgment. We demonstrate the pipeline’s validity and accuracy with multiple sets of human-expert annotations. We evaluated a diverse set of language models and retrieval systems on the benchmark and found that model performances still fall significantly short of the expert level on this task. To show the scalability of our proposed pipeline, we create a larger EvidenceBench-100k with 107,461 fully annotated papers with hypotheses to facilitate model training and development. Both datasets are available at https://github.com/EvidenceBench/EvidenceBench

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Submission Number: 1016