Are Finer Citations Always Better? Rethinking Granularity for Attributed Generation
Citation granularity -- whether to cite individual sentences, paragraphs, or documents -- is a critical design choice in attributed generation. While fine-grained citations are commonly preferred for precise human verification, their impact on model performance remains under-explored. We analyze four model scales (8B-120B) and demonstrate that enforcing fine-grained (sentence-level) citations forfeits gains of 2-97% (median 40%) relative to the best-performing granularity, and up to 338% on individual tasks. Strikingly, setting citation granularity to its optimal value (based on attribution quality) unlocks these substantial gains while leaving overall answer correctness essentially uncha...
| Authors | Hexuan Wang, Jingyu Zhang, Benjamin Van Durme et al. |
|---|---|
| Primary category | cs.CL |
| Published | 1 April 2026 |
| Last updated at source | 20 September 2026 |
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