@inproceedings{jones-etal-2026-comparative,
    title = "A Comparative Analysis of In-Context Learning and Fine-Tuning for Biomedical Information Retrieval and Sentence Extraction Using Research Domain Criteria",
    author = "Jones, Athlene  and
      Lieu, Khanh  and
      Kahanda, Indika",
    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-1.51/",
    pages = "644--655",
    ISBN = "979-8-89176-434-7",
    abstract = "Research Domain Criteria (RDoC) is a National Institute of Mental Health framework for studying mental disorders by integrating information across genetics, circuits, and behavior. Manually curating biomedical abstracts relevant to RDoC is a significant challenge due to semantically overlapping construct definitions (e.g., ``Acute Threat,'' ``Potential Threat,'' and ``Sustained Threat'') and the exponential growth of biomedical literature. This study compares two modeling strategies, domain-adapted fine-tuning and in-context prompting, across two RDoC-related subtasks from the official BioNLP-OST 2019 RDoC shared task. For Task 1, unlabeled PubMed abstracts are retrieved and ranked by relevance to eight of the RDoC constructs. We compare a TF-IDF baseline against ModernBERT and Llama (zero-shot and five-shot) using Mean Average Precision (MAP). For Task 2, the objective is to identify the single most relevant sentence from an abstract for a given construct, evaluated using per-construct accuracy. The fine-tuning track performs end-to-end fine-tuning of BioBERT, PubMedBERT, ModernBERT, and RoBERTa using a cross-encoder input format and per-construct grid search. These are compared against the in-context learning of several open-source language models. Both our approaches are competitive against the best-performing team{'}s score from the BioNLP-OST 2019 RDoC shared task. Taken together, these findings suggest that five-shot prompted LLMs and domain-adapted fine-tuned transformers are viable tools for semi-automating the expert annotation in RDoC curation."
}