Towards Reliable Medical Question Answering: Techniques and Challenges in Mitigating Hallucinations in Language Models

TL;DR

This paper surveys hallucination mitigation in large language models with a focus on medical knowledge tasks. It organizes techniques into a knowledge-grounding framework, highlighting Retrieval-Augmented Generation (RAG), iterative self-refinement, and supervised fine-tuning as pivotal approaches, while emphasizing domain-specific constraints in medicine. The authors map the landscape through a scoping study methodology, detailing search strategies, taxonomies, and end-to-end training considerations, and discuss challenges such as data quality, source authority, and real-time knowledge updating. The work argues for robust, domain-aware evaluation and real-time validation to advance trustworthy AI for clinical decision support and biomedical research, and suggests concrete directions like dynamic retrieval decisions and enhanced evaluation benchmarks to reduce medical hallucinations.

Abstract

The rapid advancement of large language models (LLMs) has significantly impacted various domains, including healthcare and biomedicine. However, the phenomenon of hallucination, where LLMs generate outputs that deviate from factual accuracy or context, poses a critical challenge, especially in high-stakes domains. This paper conducts a scoping study of existing techniques for mitigating hallucinations in knowledge-based task in general and especially for medical domains. Key methods covered in the paper include Retrieval-Augmented Generation (RAG)-based techniques, iterative feedback loops, supervised fine-tuning, and prompt engineering. These techniques, while promising in general contexts, require further adaptation and optimization for the medical domain due to its unique demands for up-to-date, specialized knowledge and strict adherence to medical guidelines. Addressing these challenges is crucial for developing trustworthy AI systems that enhance clinical decision-making and patient safety as well as accuracy of biomedical scientific research.