CiteGuard: Faithful Citation Attribution for LLMs via
  Retrieval-Augmented Validation
Authors: name: Yee Man Choi, name: Xuehang Guo, name: Yi R., name: Fung, name: Qingyun Wang
Organization: AI4Research

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.