Biomedical Relation Extraction via Adaptive Document-Relation Cross-Mapping and Concept Unique Identifier

TL;DR

This paper introduces a novel framework for document-level biomedical relation extraction that leverages large language models, synthetic data generation, cross-mapping fine-tuning, and CUI-based retrieval to improve cross-sentence inference and contextual understanding.

Contribution

The paper proposes a new LLM-based Bio-RE framework with ADRCM fine-tuning and CUI RAG retrieval, addressing data scarcity and cross-sentence inference challenges.

Findings

Achieved state-of-the-art results on GDA, CDR, and BioRED datasets.

Enhanced cross-sentence inference and contextual understanding.

Effective synthetic data generation via iterative prompts.

Abstract

Document-Level Biomedical Relation Extraction (Bio-RE) aims to identify relations between biomedical entities within extensive texts, serving as a crucial subfield of biomedical text mining. Existing Bio-RE methods struggle with cross-sentence inference, which is essential for capturing relations spanning multiple sentences. Moreover, previous methods often overlook the incompleteness of documents and lack the integration of external knowledge, limiting contextual richness. Besides, the scarcity of annotated data further hampers model training. Recent advancements in large language models (LLMs) have inspired us to explore all the above issues for document-level Bio-RE. Specifically, we propose a document-level Bio-RE framework via LLM Adaptive Document-Relation Cross-Mapping (ADRCM) Fine-Tuning and Concept Unique Identifier (CUI) Retrieval-Augmented Generation (RAG). First, we introduce the Iteration-of-REsummary (IoRs) prompt for solving the data scarcity issue. In this way, Bio-RE task-specific synthetic data can be generated by guiding ChatGPT to focus on entity relations and iteratively refining synthetic data. Next, we propose ADRCM fine-tuning, a novel fine-tuning recipe that establishes mappings across different documents and relations, enhancing the model's contextual understanding and cross-sentence inference capabilities. Finally, during the inference, a biomedical-specific RAG approach, named CUI RAG, is designed to leverage CUIs as indexes for entities, narrowing the retrieval scope and enriching the relevant document contexts. Experiments conducted on three Bio-RE datasets (GDA, CDR, and BioRED) demonstrate the state-of-the-art performance of our proposed method by comparing it with other related works.