Citation Grounding: Detecting and Reducing LLM Citation Hallucinations via Legal Citation Graphs
arXiv:2606.00898v1 Announce Type: new Abstract: Large language models systematically hallucinate legal citations -- fabricating statute references, citing repealed provisions, and confusing jurisdictions -- yet no automated method exists to measure or reduce this behavior at scale. We propose citation grounding (CG), a metric that verifies LLM-generated legal citations against a ground-truth citation graph extracted from 100.8 million Ukrainian court decisions (502 million edges, 21,736 unique statute nodes). CG decomposes into three components -- citation precision (does the cited provision e
The proliferation of LLMs in legal tech and other sensitive domains necessitates robust methods for verifying their outputs, especially citations, to prevent significant errors.
This development addresses a critical weakness of large language models—citation hallucination—which is a major barrier to their reliable deployment in high-stakes fields like law and research.
The proposed citation grounding metric offers an automated, scalable way to detect and potentially reduce LLM hallucinations, enhancing trust and enabling more practical AI applications.
- · Legal tech companies
- · AI developers
- · Legal professionals
- · Academic researchers
- · LLM providers with unaddressed hallucination issues
- · Users relying on unverified LLM outputs
Increased accuracy and trustworthiness of LLM-generated legal content and other domain-specific outputs requiring precise citations.
Accelerated adoption of LLMs in highly regulated industries as confidence in their reliability grows.
The development of similar grounding mechanisms for other forms of factual information, leading to a broader improvement in AI veracity and a new standard for AI auditability.
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Read at arXiv cs.CL