BioCoref: Benchmarking Biomedical Coreference Resolution with LLMs

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Health & Medical Research · Depth: Expert, quick

Summary

BioCoref presents a comprehensive benchmark designed to evaluate generative large language models (LLMs) for coreference resolution within the biomedical domain. This field poses unique challenges due to its complex, specialized terminology, significant ambiguity in mention forms, and the presence of long-distance dependencies between coreferring expressions. The study utilizes the CRAFT corpus as its primary benchmark to assess LLM performance. Researchers conducted four distinct prompting experiments, systematically varying the use of local and contextual enrichment, as well as domain-specific cues like abbreviations and entity dictionaries, to thoroughly test and understand the models' capabilities in this specialized and demanding area.

Key takeaway

For NLP engineers developing coreference resolution systems in the biomedical domain, understanding the impact of prompting strategies on LLM performance is critical. You should systematically experiment with incorporating local, contextual, and domain-specific cues, such as abbreviations and entity dictionaries, into your prompts. This approach will help optimize LLM accuracy for the unique challenges of biomedical texts, improving downstream application reliability.

Key insights

Biomedical coreference resolution with LLMs requires careful prompting to address domain-specific complexities like terminology and long-distance dependencies.

Principles

• Biomedical texts pose unique coreference challenges.
• Prompting strategies significantly impact LLM performance.
• Domain-specific cues are crucial for accurate resolution.

Method

The study evaluates generative LLMs on the CRAFT corpus using four prompting experiments that vary local, contextual, and domain-specific cues (abbreviations, entity dictionaries).

In practice

• Utilize the CRAFT corpus for biomedical NLP tasks.
• Experiment with varied prompting strategies for LLMs.
• Incorporate domain-specific cues like abbreviations in prompts.

Topics

• Biomedical NLP
• Coreference Resolution
• Large Language Models
• Prompt Engineering
• CRAFT Corpus
• Domain-Specific Terminology

Best for: AI Scientist, NLP Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.