# Reliable and diverse evaluation of LLM medical knowledge mastery

arXiv: https://arxiv.org/abs/2409.14302 · HTML: https://mlsanity.com/paper/2409.14302/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 26 references that are in the map.
- First posted on arXiv 22 Sep 2024.

## Builds on (most cited first, 19 of 19)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone](https://mlsanity.com/paper/2404.14219/) (2024)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams](https://mlsanity.com/paper/2009.13081/) (2020)
- [PubMedQA: A Dataset for Biomedical Research Question Answering](https://mlsanity.com/paper/1909.06146/) (2019)
- [MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering](https://mlsanity.com/paper/2203.14371/) (2022)
- [Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine](https://mlsanity.com/paper/2311.16452/) (2023)
- [MEDITRON-70B: Scaling Medical Pretraining for Large Language Models](https://mlsanity.com/paper/2311.16079/) (2023)
- [DyVal: Dynamic Evaluation of Large Language Models for Reasoning Tasks](https://mlsanity.com/paper/2309.17167/) (2023)
- [Can Language Models be Biomedical Knowledge Bases?](https://mlsanity.com/paper/2109.07154/) (2021)
- [Dynamic Evaluation of Large Language Models by Meta Probing Agents](https://mlsanity.com/paper/2402.14865/) (2024)
- [Gemini Goes to Med School: Exploring the Capabilities of Multimodal Large Language Models on Medical Challenge Problems & Hallucinations](https://mlsanity.com/paper/2402.07023/) (2024)
- [Rewire-then-Probe: A Contrastive Recipe for Probing Biomedical Knowledge of Pre-trained Language Models](https://mlsanity.com/paper/2110.08173/) (2021)
- [S3Eval: A Synthetic, Scalable, Systematic Evaluation Suite for Large Language Models](https://mlsanity.com/paper/2310.15147/) (2023)
- [CLEAN-EVAL: Clean Evaluation on Contaminated Large Language Models](https://mlsanity.com/paper/2311.09154/) (2023)
- [MultifacetEval: Multifaceted Evaluation to Probe LLMs in Mastering Medical Knowledge](https://mlsanity.com/paper/2406.02919/) (2024)

## Built on by (most cited first, 6 of 6)

- [MedLA: A Logic-Driven Multi-Agent Framework for Complex Medical Reasoning with Large Language Models](https://mlsanity.com/paper/2509.23725/) (2025)
- [Fact or Guesswork? Evaluating Large Language Models' Medical Knowledge with Structured One-Hop Judgments](https://mlsanity.com/paper/2502.14275/) (2025)
- [On the Robustness of Answer Formats in Medical Reasoning Models](https://mlsanity.com/paper/2509.20866/) (2025)
- [Diagnosing Hallucination Risk in AI Surgical Decision-Support: A Sequential Framework for Sequential Validation](https://mlsanity.com/paper/2511.00588/) (2025)
- [Beyond Captions: Context-Grounded Reconstruction for Biomedical Multimodal Continued Pretraining](https://mlsanity.com/paper/2606.01049/) (2026)
- [Applying Language Models in Clinical Medicine: Recent Trends and Perspectives](https://mlsanity.com/paper/2609.34780/) (2026)

## Abstract (arXiv)

Mastering medical knowledge is crucial for medical-specific LLMs. However, despite the existence of medical benchmarks like MedQA, a unified framework that fully leverages existing knowledge bases to evaluate LLMs' mastery of medical knowledge is still lacking. In the study, we propose a novel framework PretexEval that dynamically generates reliable and diverse test samples to evaluate LLMs for any given medical knowledge base. We notice that test samples produced directly from knowledge bases by templates or LLMs may introduce factual errors and also lack diversity. To address these issues, we introduce a novel schema into our proposed evaluation framework that employs predicate equivalence transformations to produce a series of variants for any given medical knowledge point. Finally, these produced predicate variants are converted into textual language, resulting in a series of reliable and diverse test samples to evaluate whether LLMs fully master the given medical factual knowledge point. Here, we use our proposed framework to systematically investigate the mastery of medical factual knowledge of 12 well-known LLMs, based on two knowledge bases that are crucial for clinical diagnosis and treatment. The evaluation results illustrate that current LLMs still exhibit significant deficiencies in fully mastering medical knowledge, despite achieving considerable success on some famous public benchmarks. These new findings provide valuable insights for developing medical-specific LLMs, highlighting that current LLMs urgently need to strengthen their comprehensive and in-depth mastery of medical knowledge before being applied to real-world medical scenarios.