@inproceedings{choi-etal-2026-citeguard,
    title = "{C}ite{G}uard: Faithful Citation Attribution for {LLM}s via Retrieval-Augmented Validation",
    author = "Choi, Yee Man  and
      Guo, Xuehang  and
      Fung, Yi R.  and
      Wang, Qingyun",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.282/",
    pages = "6241--6257",
    ISBN = "979-8-89176-390-6",
    abstract = "Large Language Models (LLMs) have emerged as powerful assistants for scientific writing. However, concerns remain about the quality and reliability of the generated text, including citation accuracy and faithfulness. While most recent work relies on methods such as LLM-as-a-Judge, the reliability of LLM-as-a-Judge alone is also in doubt. In this work, we reframe citation evaluation as a problem of citation attribution alignment, which assesses whether LLM-generated citations match those a human author would include for the same text. We propose CiteGuard, a retrieval-aware agent framework designed to provide more faithful grounding for citation validation. CiteGuard improves over the prior baseline by 10 percentage points and achieves up to 68.1{\%} accuracy on the CiteME benchmark, approaching human performance (69.2{\%}). It also identifies alternative valid citations and demonstrates generalization ability for cross-domain citation attribution."
}