GLiM: Integrating Graph Transformer and LLM for Document-Level Biomedical Relation Extraction with Incomplete Labeling

Document-level relation extraction (DocRE) identifies relations between entities across an entire document.However, as the number and complexity of entities and entity-pair relations grow, the problem space expands quadratically, causing incomplete annotations and frequent false negatives, especially in biomedical datasets due to high construction costs.This leads to low recall in real-world scenarios.To address this, we propose GLiM, a novel framework that reduces the problem space using a graph-enhanced Transformer-based model and leverages large language models (LLMs) for reasoning.GLiM employs a cascaded approach: first, a graph-enhanced Transformer processes entity-pair relations with finer granularity by dynamically adjusting the graph size based on the number of entities; then, LLM inference handles challenging cases.Experiments show that GLiM boosts average recall and F1 scores by +6.34 and +4.41, respectively, outperforming state-of-the-art models on biomedical benchmarks.These results demonstrate the effectiveness of combining graphenhanced Transformers with LLM inference for biomedical DocRE.Code will be released at https://github.com/HaoFang10/GLiM.