PlugMed: Improving Specificity in Patient-Centered Medical Dialogue Generation using In-Context Learning

TL;DR

PlugMed enhances patient-centered medical dialogue generation by using in-context learning, prompt generation, and response ranking modules to improve response specificity, validated through experiments on multiple datasets.

Contribution

The paper introduces PlugMed, a novel plug-and-play system with prompt generation and response ranking modules to improve specificity in medical dialogue generation.

Findings

Significant improvement in response specificity demonstrated.

Effective use of in-context learning for medical dialogues.

Validated by both automatic and human evaluations.

Abstract

The patient-centered medical dialogue systems strive to offer diagnostic interpretation services to users who are less knowledgeable about medical knowledge, through emphasizing the importance of providing responses specific to the patients. It is difficult for the large language models (LLMs) to guarantee the specificity of responses in spite of its promising performance even in some tasks in medical field. Inspired by in-context learning, we propose PlugMed, a Plug-and-Play Medical Dialogue System, for addressing this challenge. PlugMed is equipped with two modules, the prompt generation (PG) module and the response ranking (RR) module, to enhances LLMs' dialogue strategies for improving the specificity of the dialogue. The PG module is designed to stimulate the imitative ability of LLMs by providing them with real dialogues from similar patients as prompts. The RR module incorporates fine-tuned small model as response filter to enable the selection of appropriate responses generated by LLMs. Furthermore, we introduce a new evaluation method based on matching both user's intent and high-frequency medical term to effectively assess the specificity of the responses. We conduct experimental evaluations on three medical dialogue datasets, and the results, including both automatic and human evaluation, demonstrate the effectiveness of our approach.