Explicit Evidence Grounding via Structured Inline Citation Generation

2026-06-05

AI Summary

Large language models often struggle to precisely cite supporting evidence when generating answers, compromising factual accuracy and traceability. To address this, this work proposes FullCite, a framework that achieves dual alignment of each claim to both its source document and the specific evidential span within it—the first approach to do so. FullCite integrates three strategies: prompt-based generation, citation-aware constrained decoding, and post-hoc span alignment, to produce structured inline citations. Experiments on ASQA, BioASQ, and ExpertQA demonstrate that while large models effectively retrieve relevant documents, they exhibit significant limitations in pinpointing exact supporting evidence spans. FullCite substantially improves output quality across three key dimensions: document relevance, evidence span accuracy, and claim faithfulness.

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.

Problem

• evidence grounding
• inline citation
• faithful generation
• evidence span identification
• attributed QA

Innovation

• structured inline citation
• evidence grounding
• citation generation
• faithful generation
• span alignment