Satisfactory Medical Consultation based on Terminology-Enhanced Information Retrieval and Emotional In-Context Learning
Journal:
arXiv
Published Date:
Mar 22, 2025
Abstract
Recent advancements in Large Language Models (LLMs) have marked significant
progress in understanding and responding to medical inquiries. However, their
performance still falls short of the standards set by professional
consultations. This paper introduces a novel framework for medical
consultation, comprising two main modules: Terminology-Enhanced Information
Retrieval (TEIR) and Emotional In-Context Learning (EICL). TEIR ensures
implicit reasoning through the utilization of inductive knowledge and key
terminology retrieval, overcoming the limitations of restricted domain
knowledge in public databases. Additionally, this module features capabilities
for processing long context. The EICL module aids in generating sentences with
high attribute relevance by memorizing semantic and attribute information from
unlabelled corpora and applying controlled retrieval for the required
information. Furthermore, a dataset comprising 803,564 consultation records was
compiled in China, significantly enhancing the model's capability for complex
dialogues and proactive inquiry initiation. Comprehensive experiments
demonstrate the proposed method's effectiveness in extending the context window
length of existing LLMs. The experimental outcomes and extensive data validate
the framework's superiority over five baseline models in terms of BLEU and
ROUGE performance metrics, with substantial leads in certain capabilities.
Notably, ablation studies confirm the significance of the TEIR and EICL
components. In addition, our new framework has the potential to significantly
improve patient satisfaction in real clinical consulting situations.
