Large language models are playing an increasingly significant role in
molecular research, yet existing models often generate erroneous information,
posing challenges to accurate molecular comprehension. Traditional evaluation
metrics for generated content fail to assess a model's accuracy in molecular
understanding. To rectify the absence of factual evaluation, we present
MoleculeQA, a novel question answering (QA) dataset which possesses 62K QA
pairs over 23K molecules. Each QA pair, composed of a manual question, a
positive option and three negative options, has consistent semantics with a
molecular description from authoritative molecular corpus. MoleculeQA is not
only the first benchmark for molecular factual bias evaluation but also the
largest QA dataset for molecular research. A comprehensive evaluation on
MoleculeQA for existing molecular LLMs exposes their deficiencies in specific
areas and pinpoints several particularly crucial factors for molecular
understanding.

MoleculeQA 是一个用于分子研究的新型问题回答数据集，通过在每个问答对中提供手动问题、一个正选项和三个负选项，并与权威的分子语料库中的分子描述具有一致的语义，首次评估了现有分子语言模型在分子事实方面的准确性，并揭示了其中的不足之处和分子理解的几个关键因素。