CiteGuard: Faithful Citation Attribution for LLMs via
  Retrieval-Augmented Validation
Large Language Models (LLMs) have emerged as promising assistants for
scientific writing. However, there have been concerns regarding the quality and
reliability of the generated text, one of which is the 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 is assessing 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 the prior baseline by 12.3%, and achieves up to 65.4%
accuracy on the CiteME benchmark, on par with human-level performance (69.7%).
It also enables the identification of alternative but valid citations.
68fb9dd56cdff8b857f46bbb
Yee Man Choi
claimed_verified
2025-10-24T15:57:30.873Z
65562c0d1f308b7658ee2e1c
/avatars/c9619dc81555d93f9720eb10b733202c.svg
Kath Choi
user
KathCYM
68fb9dd56cdff8b857f46bbc
Xuehang Guo
68fb9dd56cdff8b857f46bbd
Yi R.
68fb9dd56cdff8b857f46bbe
Fung
68fb9dd56cdff8b857f46bbf
Qingyun Wang
claimed_verified
2025-10-24T15:57:33.565Z
628ba530ac304a69264afb75
https://cdn-avatars.huggingface.co/v1/production/uploads/1653319185560-628ba530ac304a69264afb75.jpeg
Qingyun Wang
user
eaglew
CiteGuard: Faithful Citation Attribution for LLMs via
  Retrieval-Augmented Validation
Large Language Models (LLMs) have emerged as promising assistants for
scientific writing. However, there have been concerns regarding the quality and
reliability of the generated text, one of which is the 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 is assessing 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 the prior baseline by 12.3%, and achieves up to 65.4%
accuracy on the CiteME benchmark, on par with human-level performance (69.7%).
It also enables the identification of alternative but valid citations.
CiteGuard, a retrieval-aware agent framework, enhances citation accuracy in LLM-generated text by aligning citations with human choices, achieving near-human performance.
Large Language Models LLM-as-a-Judge citation evaluation citation attribution alignment retrieval-aware agent CiteGuard CiteME benchmark