Tool Calling: Enhancing Medication Consultation via Retrieval-Augmented Large Language Models

Understand

Large-scale language models (LLMs) have achieved remarkable success across various language tasks but suffer from hallucinations and temporal misalignment.

• To mitigate these shortcomings, Retrieval-augmented generation (RAG) has been utilized to provide external knowledge to facilitate the answer generation.
• However, applying such models to the medical domain faces several challenges due to the lack of domain-specific knowledge and the intricacy of real-world scenarios.
• In this study, we explore LLMs with RAG framework for knowledge-intensive tasks in the medical field.