Evaluating Large Language Models on Misconceptions in Multi-Turn Medical Conversations

2025-08-07 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Health & Medical Research · Depth: Expert, long

Summary

A new dataset, ThReadMed-QA, has been introduced to evaluate large language models' ability to identify and correct medical misconceptions in multi-turn patient-physician conversations. Comprising 2,437 conversation threads and 8,204 question-answer pairs derived from real patient interactions on r/AskDocs, the dataset enables systematic assessment of sustained misconception correction. Evaluation of five state-of-the-art LLMs, including GPT-5, Claude-Haiku, and GPT-4o, revealed a consistent degradation in performance. While GPT-5 and Claude-Haiku corrected false presuppositions around 85% on initial questions, this dropped to approximately 50% within two follow-ups. GPT-4o showed an even sharper decline, falling from 65% to 21%. An oracle analysis indicated that error propagation significantly drives this degradation, highlighting a critical reliability gap in LLMs for safe, consistent medical guidance over extended dialogues.

Key takeaway

For AI Scientists and NLP Engineers developing patient-facing LLMs, you must prioritize multi-turn reliability. Your models' ability to correct medical misconceptions degrades sharply over dialogue, risking unsafe guidance. Focus on mitigating error propagation and developing evaluation frameworks that capture sustained reasoning across turns. Consider using the ThReadMed-QA dataset and its evaluation methodology to benchmark and improve your model's conversational robustness.

Key insights

LLMs struggle to consistently correct medical misconceptions in multi-turn dialogues due to performance degradation and error propagation.

Principles

• Misconception correction degrades significantly over multiple turns.
• Error propagation drives much of multi-turn performance decline.
• Evaluation frameworks must capture multi-turn interaction dynamics.

Method

The study introduces ThReadMed-QA, a multi-turn medical dialogue dataset from r/AskDocs. It uses an LLM-as-a-Judge framework with a 3-point rubric to score misconception correction, evaluating models sequentially with full conversational context.

In practice

• Use ThReadMed-QA to benchmark multi-turn misconception handling.
• Implement oracle-style ablation to diagnose error propagation.
• Develop models robust to error propagation in dialogue.

Topics

• Large Language Models
• Medical Misconceptions
• Multi-turn Dialogue
• ThReadMed-QA Dataset
• LLM Evaluation
• Healthcare AI

Code references

• monicamunnangi/ThReadMed-QA-Misc

Best for: AI Scientist, Research Scientist, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.