MoleculeQA: A Dataset to Evaluate Factual Accuracy in Molecular
  Comprehension
Xingyu Lu He Cao Zijing Liu Shengyuan Bai Leqing Chen Yuan Yao Hai-Tao Zheng Yu Li
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.