  Ensuring the accuracy of responses provided by large language models (LLMs)
is crucial, particularly in clinical settings where incorrect information may
directly impact patient health. To address this challenge, we construct K-QA, a
dataset containing 1,212 patient questions originating from real-world
conversations held on K Health (an AI-driven clinical platform). We employ a
panel of in-house physicians to answer and manually decompose a subset of K-QA
into self-contained statements. Additionally, we formulate two NLI-based
evaluation metrics approximating recall and precision: (1) comprehensiveness,
measuring the percentage of essential clinical information in the generated
answer and (2) hallucination rate, measuring the number of statements from the
physician-curated response contradicted by the LLM answer. Finally, we use K-QA
along with these metrics to evaluate several state-of-the-art models, as well
as the effect of in-context learning and medically-oriented augmented retrieval
schemes developed by the authors. Our findings indicate that in-context
learning improves the comprehensiveness of the models, and augmented retrieval
is effective in reducing hallucinations. We make K-QA available to to the
community to spur research into medically accurate NLP applications.
