Guiding Large Language Models for Biomedical Entity Linking via Restrictive and Contrastive Decoding (2025)

Abstract: Biomedical entity linking (BioEL) aims at mapping biomedical mentions to pre-defined entities. While extensive research efforts have been devoted to BioEL, applying large language models (LLMs) for BioEL has not been fully explored. Previous attempts have revealed difficulties when directly applying LLMs to the task of BioEL. Possible errors include generating non-entity sentences, invalid entities, or incorrect answers. To this end, we introduce LLM4BioEL, a concise yet effective framework that enables LLMs to adapt well to the BioEL task. LLM4BioEL employs restrictive decoding to ensure the generation of valid entities and utilizes entropy-based contrastive decoding to incorporate additional biomedical knowledge without requiring further tuning. Besides, we implement few-shot prompting to maximize the in-context learning capabilities of LLM. Extensive experiments demonstrate the effectiveness and applicability of LLM4BioEL across different BioEL tasks and with different LLM backbones, and the best-performing LLM4BioEL variant outperforms the traditional and LLM-based BioEL baselines.

## introduction biomedical entity linking (bioel) serves as the foundation for tasks like biomedical kg construction @xcite , kg-based answering @xcite , and automatic diagnosis @xcite @xcite . bioel aims at recognizing the biomedical mentions and linking them to standard entities with valid concept unique ids (cuis) in the given medical knowledge graphs (kgs), such as umls @xcite . in contrast to the general-domain entity linking, bioel is * these authors contributed equally to this work. ## conclusion in this paper, we introduced llm4bioel, a straightforward yet effective framework for bioel. llm4bioel consists of restrictive decoding to ensure valid output, entropy-based contrastive decoding to adaptively integrate external knowledge, and few-shot prompting to enhance the linking performance. we experimented with four different llms and validated the performance improvement of llm4bioel compared to other baselines. we also demonstrated its comparative performance to other state-of-the-art bioel baselines, and its flexibility to switch between different tasks and backbones without fine-tuning. for future exploration, we plan to investigate llm4bioel in the general-domain el and investigate the combination of decoding strategies for other knowledge-intensive tasks.