CiteGuard: Faithful Citation Attribution for LLMs via Retrieval-Augmented Validation

ACL ARR 2026 January Submission6718 Authors

Keywords: Citation Attribution, AI4Research

Abstract: 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 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 the prior baseline by 17%, and achieves up to 68.1% accuracy on the CiteME benchmark, approaching human-level performance (69.7%). It also enables the identification of alternative but valid citations and demonstrates generalization ability for cross-domain citation attribution.

Paper Type: Long

Research Area: Resources and Evaluation

Research Area Keywords: Resources and Evaluation, AI / LLM Agents

Contribution Types: NLP engineering experiment, Publicly available software and/or pre-trained models

Languages Studied: English

Submission Number: 6718