Comprehensive and Practical Evaluation of Retrieval-Augmented Generation Systems for Medical Question Answering
Nghia Trung Ngo, Chien Van Nguyen, Franck Dernoncourt, Thien Huu Nguyen
2024-11-19
Summary
This paper discusses a new evaluation framework for medical question-answering systems that use retrieval-augmented generation (RAG) to ensure accurate and reliable answers in the medical field.
What's the problem?
While retrieval-augmented generation has shown promise in improving the performance of large language models for medical tasks, existing benchmarks often focus only on basic retrieval and answering. They fail to consider important real-world scenarios where accuracy and trustworthiness are critical, especially in medicine, where incorrect information can have serious consequences.
What's the solution?
To address these issues, the authors introduce the Medical Retrieval-Augmented Generation Benchmark (MedRGB), which provides a comprehensive way to evaluate how well medical question-answering systems perform under various conditions. This framework includes testing for aspects like sufficiency, integration, and robustness. They conducted extensive evaluations of both commercial and open-source models using MedRGB, revealing that many current models struggle with handling noise and misinformation in retrieved documents.
Why it matters?
This research is significant because it aims to improve the reliability of AI systems used in healthcare. By providing a more thorough evaluation framework, MedRGB helps ensure that medical question-answering systems can deliver accurate information when it matters most, ultimately enhancing patient care and supporting healthcare professionals.
Abstract
Retrieval-augmented generation (RAG) has emerged as a promising approach to enhance the performance of large language models (LLMs) in knowledge-intensive tasks such as those from medical domain. However, the sensitive nature of the medical domain necessitates a completely accurate and trustworthy system. While existing RAG benchmarks primarily focus on the standard retrieve-answer setting, they overlook many practical scenarios that measure crucial aspects of a reliable medical system. This paper addresses this gap by providing a comprehensive evaluation framework for medical question-answering (QA) systems in a RAG setting for these situations, including sufficiency, integration, and robustness. We introduce Medical Retrieval-Augmented Generation Benchmark (MedRGB) that provides various supplementary elements to four medical QA datasets for testing LLMs' ability to handle these specific scenarios. Utilizing MedRGB, we conduct extensive evaluations of both state-of-the-art commercial LLMs and open-source models across multiple retrieval conditions. Our experimental results reveals current models' limited ability to handle noise and misinformation in the retrieved documents. We further analyze the LLMs' reasoning processes to provides valuable insights and future directions for developing RAG systems in this critical medical domain.