中国科学院自动化研究所机构知识库

Large language models (LLMs) excel at providing general and comprehensive health advice in single-turn dialogues. However, the limited information in single-turn conversations provided by users results in generated advice lacking personalization and specificity. In real-world medical consultations, doctors typically gain a comprehensive understanding of a patient's condition through a series of iterative inquiries, enabling them to subsequently offer effective and personalized advice. To enhance capabilities similar to those of doctors, existing approaches often learn by increasing multi-turn medical dialogue corpora. In this study, we consider capturing the transitions of medical terms in each turn crucial, as they aid in understanding the flow of the conversation and enhance the accuracy of generating medical term information in the next turn. Therefore, we propose a Term-aware Agent (T-Agent) and develop a corresponding term extraction tool and term prediction model. T-Agent explicitly models the flow of term information in the dialogue by invoking the term extraction tool and the term prediction model. To better learn the term prediction task, we adopt a two-stage training approach. In the first stage, we conduct mixed training
on a single large model, simultaneously learning term prediction and the ability of T-Agent to invoke term tools for dialogue. This mixed training in the first stage allows the large model to initially adapt to the term prediction task. In the second stage, we independently train the term prediction model and TAgent on this basis, enhancing their expertise and performance in their respective tasks. We validated the effectiveness of the proposed method on two Chinese multi-turn medical dialogue
datasets, demonstrating significant performance improvements, particularly in the accuracy of term information within dialogues.