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Data Citation for Large Language Models: A Challenge — arXiv:2608.25663

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.25663
License (as archived): CC0-1.0
Archived: 2026-10-18T01:29:31.195Z
SHA-256 of the archived text: 3be19231837e0bac8973cdfbeb6bb6a44f22aa4437413bf223b53ea22018fcc6
Stable URL of this page: https://metraipolis.com/documento/dc_488e415da35c

How to cite

Data Citation for Large Language Models: A Challenge — arXiv:2608.25663 — arXiv (en), https://arxiv.org/abs/2608.25663 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_488e415da35c (Metraipolis archive dc_488e415da35c, sha256 3be19231837e0bac8973cdfbeb6bb6a44f22aa4437413bf223b53ea22018fcc6)

BibTeX

@misc{dc488e415da35c,
  title = {Data Citation for Large Language Models: A Challenge — arXiv:2608.25663},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.25663},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_488e415da35c; sha256 3be19231837e0bac8973cdfbeb6bb6a44f22aa4437413bf223b53ea22018fcc6},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.25663. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Large language models increasingly mediate access to information, and a growing body of work asks whether they cite the sources behind their outputs. That work treats citation as a verification device and applies it to textual documents. Scholarly citation serves two further functions, credit and provenance, and it applies to data as much as to text. This paper argues that data citation for large language models is an open challenge, distinct from document-level citation grounding and harder to solve. We ask how such models should cite data so that outputs stay verifiable, provenance stays traceable, and credit reaches data creators and curators. We set out three research directions. Training data attribution has to turn influence estimates into references for corpora absorbed into model parameters. Data citation at inference time has to identify datasets, subsets, and query results at the right granularity and fixity. Citing knowledge graph facts has to define what a reference to a single triple denotes and how credit propagates along provenance. Progress on all three depends on joint work across the database, information retrieval, knowledge representation, and artificial intelligence communities.

Tema degli esploratori: ricerca e scienza aperta (retro-catalogo arXiv: annunci del 2026-08-27 in cs.DL).
Categorie arXiv: Information Retrieval (cs.IR) ; Artificial Intelligence (cs.AI); Databases (cs.DB); Digital Libraries (cs.DL).
Pagina del lavoro: https://arxiv.org/abs/2608.25663 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-18T01:27:21.259Z dalla pagina https://arxiv.org/catchup/cs.DL/2026-08-27?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).