Evaluating the Performance of Large Language Models on Palliative Care Test Questions: A Mixed Methods Study

Abstract

Background:

Little is known about large language model (LLM) performance on palliative care (PC)-related knowledge-based tasks. We evaluated two LLMs in answering PC-related test questions and explaining their answer choice rationale.

Methods:

LLMs were prompted to answer 25 randomly selected questions from the Fast Facts Quiz and provide their answer choice rationale. Three PC educators ranked and rated LLM-generated answer choice explanations versus the test’s answer key explanations. Linear fixed-effect models evaluated reviewer ranking, and ordinal logistic regression evaluated reviewer ratings of quality, suitability, accuracy, relevance, and comprehensiveness.

Results:

Both LLMs answered 96% of selected questions correctly. Reviewers rated LLM-generated explanations more highly than Fast Facts Quiz explanations. Five themes emerged from reviewer comments: perceived inaccuracies, clarity of writing, educational value, linguistic style, and miscellaneous.

Conclusions:

LLMs demonstrated high answer choice accuracy and generated preferable answer explanations when compared to the Fast Facts Quiz answer key.