@inproceedings{fang-etal-2025-glim,
    title = "{GL}i{M}: Integrating Graph Transformer and {LLM} for Document-Level Biomedical Relation Extraction with Incomplete Labeling",
    author = "Fang, Hao  and
      Zhang, Yuejie  and
      Feng, Rui  and
      Wang, Yingwen  and
      Wang, Qing  and
      He, Wen  and
      Zhang, Xiaobo  and
      Zhang, Tao  and
      Gao, Shang",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/mtsummit-25-ingestion/2025.findings-acl.727/",
    doi = "10.18653/v1/2025.findings-acl.727",
    pages = "14131--14146",
    ISBN = "979-8-89176-256-5",
    abstract = "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 graph-enhanced Transformers with LLM inference for biomedical DocRE. Code will be released at https://github.com/HaoFang10/GLiM."
}