@inproceedings{jiang-kavuluru-2026-graft,
    title = "{GRAFT}: Gated Retrieval-Augmented Fine-Tuning for Relation Extraction",
    author = "Jiang, Yuhang  and
      Kavuluru, Ramakanth",
    editor = "Demner-Fushman, Dina  and
      Ananiadou, Sophia  and
      Roberts, Kirk  and
      Tsujii, Junichi",
    booktitle = "{B}io{NLP} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.bionlp-1.74/",
    pages = "920--931",
    ISBN = "979-8-89176-434-7",
    abstract = "Even in the era of large language models (LLMs), biomedical relation extraction (RE) still plays a major role in timely creation of knowledge graphs that further guide biomedical knowledge discovery. The main task in RE is to extract a relation ``as expressed'' in an input text. At times, crucial definitional information or other auxiliary information about the entities involved may be missing from the input text. Augmenting it from other external textual sources appears helpful on the surface but can be harmful too, as these sources can overwhelm the signal in the original input, leading to false positives or false negatives. To counter this, we leverage a pre-trained biomedical text retriever to augment original inputs with additional instance-specific snippets. This is done through a gating mechanism that allows the retrieved snippets to enhance but not overwhelm the signal from the original input. We evaluate our approach on three standard biomedical relation extraction datasets (CDR, BioRED, and ChemProt) and show consistent improvements (up to 10 F1 points) compared with strong supervised baselines involving both encoder and decoder models. All our code and the datasets used are available for reuse: {\textbackslash}url{\{}https://github.com/bionlproc/GRAFT-RE{\}}."
}