Mitigating Hallucinations of Large Language Models in Medical Information Extraction via Contrastive Decoding (2024)

Abstract: The impressive capabilities of large language models (LLMs) have attracted extensive interests of applying LLMs to medical field. However, the complex nature of clinical environments presents significant hallucination challenges for LLMs, hindering their widespread adoption. In this paper, we address these hallucination issues in the context of Medical Information Extraction (MIE) tasks by introducing ALternate Contrastive Decoding (ALCD). We begin by redefining MIE tasks as an identify-and-classify process. We then separate the identification and classification functions of LLMs by selectively masking the optimization of tokens during fine-tuning. During the inference stage, we alternately contrast output distributions derived from sub-task models. This approach aims to selectively enhance the identification and classification capabilities while minimizing the influence of other inherent abilities in LLMs. Additionally, we propose an alternate adaptive constraint strategy to more effectively adjust the scale and scope of contrastive tokens. Through comprehensive experiments on two different backbones and six diverse medical information extraction tasks, ALCD demonstrates significant improvements in resolving hallucination issues compared to conventional decoding methods.

## introduction medical information extraction (mie), including tasks such as medical entity recognition and relation extraction, is a fundamental component of medical nlp @xcite . it enables the derivation of structured knowledge from plain text, benefiting a wide array of applications @xcite @xcite , like medical knowledge graph construction @xcite @xcite , medical dialogue @xcite ## methodology in this section, we introduce alternate contrastive decoding (alcd), a method specifically designed for medical information extraction tasks. section 3.1 provides the foundational knowledge of contrastive decoding, while section 3.2 delves into the details of our proposed alcd method. ## limitation our approach aims to decouple the identification and classification abilities of llms in the medical information extraction tasks and leverage their respective capabilities through alternate contrastive decoding. however, this strategy leads to an increase in both fine-tuning and inference costs. in this paper, alcd switches between identification or classification capabilities based on simple rulebased judgment, but it is worth exploring more automatic and flexible judgment methods in future work. furthermore, we have only investigated the effectiveness of our approach in medical information extraction tasks, and expanding our alcd framework to other medical tasks, other domains, and other language settings is an avenue for future exploration. exploring more robust decoupling methods and contrasting decoding techniques are also potential future research directions.