PretextTrans: Investigating Medical Factual Knowledge Mastery of LLMs with Predicate-text Dual Transformation

In the study, we aim to investigate current LLMs' mastery of medical factual knowledge with a dynamic evaluation schema, which can automatically generate multiple test samples for each medical factual knowledge point. Test samples produced directly by LLMs always introduce factual errors and lack diversity in the manner of knowledge expression. To overcome the drawbacks, here we propose a novel evaluation method, Predicate-text Dual Transformation (PretextTrans), by introducing predicate transformations into the dynamic evaluation schema. Specifically, each medical knowledge point is firstly transformed into a predicate expression; then, the predicate expression derives a series of variants through predicate transformations; lastly, the produced predicate variants are transformed back into textual expressions, resulting in a series of test samples with both factual reliability and expression diversity. Using the proposed PretextTrans method, we systematically investigate 12 well-known LLMs' mastery of medical factual knowledge based on two medical datasets. The comparison results show that current LLMs still have significant deficiencies in fully mastering medical knowledge, which may illustrate why current LLMs still perform unsatisfactorily in real-world medical scenarios despite having achieved considerable performance on public benchmarks. Our proposed method serves as an effective solution for evaluation of LLMs in medical domain and offers valuable insights for developing medical-specific LLMs.

MultifacetEval: Multifaceted Evaluation to Probe LLMs in Mastering Medical Knowledge

Large language models (LLMs) have excelled across domains, also delivering notable performance on the medical evaluation benchmarks, such as MedQA. However, there still exists a significant gap between the reported performance and the practical effectiveness in real-world medical scenarios. In this paper, we aim to explore the causes of this gap by employing a multifaceted examination schema to systematically probe the actual mastery of medical knowledge by current LLMs. Specifically, we develop a novel evaluation framework MultifacetEval to examine the degree and coverage of LLMs in encoding and mastering medical knowledge at multiple facets (comparison, rectification, discrimination, and verification) concurrently. Based on the MultifacetEval framework, we construct two multifaceted evaluation datasets: MultiDiseK (by producing questions from a clinical disease knowledge base) and MultiMedQA (by rephrasing each question from a medical benchmark MedQA into multifaceted questions). The experimental results on these multifaceted datasets demonstrate that the extent of current LLMs in mastering medical knowledge is far below their performance on existing medical benchmarks, suggesting that they lack depth, precision, and comprehensiveness in mastering medical knowledge. Consequently, current LLMs are not yet ready for application in real-world medical tasks. The codes and datasets are available at https://github.com/THUMLP/MultifacetEval.