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

arXiv:2404.17897v1 Announce Type: new
Abstract: 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 …

abstract arxiv cs.cl hallucinations however knowledge language language models large language large language models llms medical rag retrieval retrieval-augmented scale success tasks temporal tool type via