# An Empirical Evaluation of Large Language Models on Consumer Health Questions Source: [An Empirical Evaluation of Large Language Models on Consumer Health Questions]() Collection index: [llms.txt]() Data updated: 2025-10-26T03:53:27.652Z Authors: Moaiz Abrar, Yusuf Sermet, Ibrahim Demir Published: 2024-12-31T01:08:15.000Z ## Abstract This study evaluates the performance of several Large Language Models (LLMs) on MedRedQA, a dataset of consumer-based medical questions and answers by verified experts extracted from the AskDocs subreddit. While LLMs have shown proficiency in clinical question answering (QA) benchmarks, their effectiveness on real-world, consumer-based, medical questions remains less understood. MedRedQA presents unique challenges, such as informal language and the need for precise responses suited to non-specialist queries. To assess model performance, responses were generated using five LLMs: GPT-4o mini, Llama 3.1: 70B, Mistral-123B, Mistral-7B, and Gemini-Flash. A cross-evaluation method was used, where each model evaluated its responses as well as those of others to minimize bias. The results indicated that GPT-4o mini achieved the highest alignment with expert responses according to four out of the five models' judges, while Mistral-7B scored lowest according to three out of five models' judges. This study highlights the potential and limitations of current LLMs for consumer health medical question answering, indicating avenues for further development. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()