@inproceedings{ma-etal-2026-vericite,
    title = "{VERICITE}: Evaluating Sentence-Level Citation Faithfulness in Retrieval-Augmented Medical Question Answering",
    author = "Ma, Yixian  and
      Chu, Bohao  and
      Fuhr, Norbert",
    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-1.62/",
    pages = "753--759",
    ISBN = "979-8-89176-434-7",
    abstract = "Retrieval-augmented generation (RAG) reduces hallucination in large language models by grounding outputs in retrieved evidence, but it does not guarantee that the resulting citations support the associated claims. We present VERICITE, a framework for evaluating citation faithfulness in retrieval-augmented medical QA. Our system retrieves PubMed abstracts via the NCBI E-Utilities API, prompts LLMs to generate answers with inline citations, and verifies each citation at the sentence level using a DeBERTa-v3-large NLI model. We evaluate four LLMs on 500 BioASQ questions at retrieval depths of 3 and 5, with extended experiments up to k = 15 and an oracle setting with gold standard documents. Only 27?41{\%} of citation pairs are supported at the sentence level at retrieval depths of 3 and 5, with support rates declining further at larger k. Under the oracle condition, answer quality improves, but citation faithfulness does not substantially improve, suggesting that generation-side citation behavior contributes substantially to unfaithful citations."
}