Triplet-Structured Knowledge Integration for Multi-Turn Medical Reasoning

• URL: http://arxiv.org/abs/2510.03536v2
• Date: Tue, 14 Oct 2025 11:40:04 GMT
• Title: Triplet-Structured Knowledge Integration for Multi-Turn Medical Reasoning
• Authors: Zhaohan Meng, Zaiqiao Meng, Siwei Liu, Iadh Ounis,
• Abstract summary: Large Language Models (LLMs) have shown strong performance on static medical Question Answering (QA) tasks.<n>This paper introduces TriMediQ, a triplet-structured approach that enhances the reasoning reliability of LLMs.<n> Experiments on two interactive medical QA benchmarks show that TriMediQ achieves up to 10.4% improvement in accuracy over five existing baselines.
• Score: 21.44813166265882
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have shown strong performance on static medical Question Answering (QA) tasks, yet their reasoning often deteriorates in multi-turn clinical dialogues where patient information is scattered across turns. This paper introduces TriMediQ, a triplet-structured approach that enhances the reasoning reliability of LLMs through explicit knowledge integration. TriMediQ first employs a frozen triplet extraction LLM to convert patient responses into clinically grounded triplets, ensuring factual precision via constrained prompting. These triplets are incorporated into a patient-specific Knowledge Graph (KG), from which a trainable projection module consisting of a graph encoder and a projector captures relational dependencies while keeping all LLM parameters frozen. During inference, the projection module guides multi-hop reasoning over the KG, enabling coherent clinical dialogue understanding. Experiments on two interactive medical QA benchmarks show that TriMediQ achieves up to 10.4\% improvement in accuracy over five existing baselines on the iMedQA dataset. These results demonstrate that structuring patient information as triplets can effectively improve the reasoning capability of LLMs in multi-turn medical QA.

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