Multiple Choice Questions and Large Languages Models: A Case Study with Fictional Medical Data

Maxime Griot, Jean Vanderdonckt, Demet YUKSEL, Coralie Hemptinne

Keywords: large language models, medicine, benchmark, evaluation, clinical knowledge, multiple choice questions

TL;DR: Evaluating LLMs with fictional medical benchmarks reveals traditional MCQs assess pattern recognition over clinical knowledge, indicating a need for improved evaluation methods.

Abstract: Large Language Models (LLMs) like ChatGPT demonstrate significant potential in the medical field, often evaluated using multiple-choice questions (MCQs) similar to those found on the USMLE. Despite their prevalence in medical education, MCQs have limitations that might be exacerbated when assessing LLMs. To evaluate the effectiveness of MCQs in assessing the performance of LLMs, we developed a fictional medical benchmark focused on a non-existent gland, the Glianorex. This approach allowed us to isolate the knowledge of the LLM from its test-taking abilities. We used GPT-4-Turbo and Claude 3.5 Sonnet to generate two comprehensive textbooks on the Glianorex in both English and French and developed corresponding multiple-choice questions in both languages. We evaluated various open-source, proprietary, and domain-specific LLMs using these questions in a zero-shot setting. The models achieved average scores around 64%, with minor performance differences between larger and smaller models. Performance was slightly higher in English than in French. Fine-tuned medical models showed some improvement over their base versions in English but not in French. The high performance across models suggests that traditional MCQ-based benchmarks may not accurately measure LLMs' clinical knowledge and reasoning abilities, instead highlighting their pattern recognition skills. This study underscores the need for more robust evaluation methods to better assess the true capabilities of LLMs in medical contexts.

Primary Area: datasets and benchmarks

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Submission Number: 7433