Explicit Evidence Grounding via Structured Inline Citation Generation

Abstract:As AI systems become more widely adopted, the demand for factual and faithful generation grows. Properly attributing information through citations becomes, therefore, crucial. This work introduces FullCite, a framework that, in contrast to most previous works, generates structured inline citations linking each claim to both its source document and supporting evidence. FullCite proposes three strategies to inline citation generation: prompt-based generation, constrained decoding over a citation grammar, and posthoc span alignment. Using three question answering benchmarks, namely, ASQA, BioASQ, and ExpertQA, we assess citation quality and faithfulness along three dimensions: document-level correctness, evidence span identification, and claim-citation faithfulness. Our evaluation shows that while LLMs are generally effective at identifying relevant documents, they struggle to identify the precise supporting spans within them. This gap suggests that achieving faithful attributed QA will require research to place greater emphasis on precise evidence span identification.

Computation and Language (cs.CL)

arXiv:2606.07130 [cs.CL]

(or arXiv:2606.07130v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2606.07130 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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