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Beyond Tables: Doc2DB-Bench for Relationally Faithful Document-to-Database Construction — arXiv:2608.08459

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.08459
License (as archived): CC0-1.0
Archived: 2026-10-19T08:49:59.104Z
SHA-256 of the archived text: 91bca926e7459a6c6a23b5166968be135f74be120e8ab8830bc4501d21fe6868
Stable URL of this page: https://metraipolis.com/documento/dc_315e6bb7b024

How to cite

Beyond Tables: Doc2DB-Bench for Relationally Faithful Document-to-Database Construction — arXiv:2608.08459 — arXiv (en), https://arxiv.org/abs/2608.08459 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_315e6bb7b024 (Metraipolis archive dc_315e6bb7b024, sha256 91bca926e7459a6c6a23b5166968be135f74be120e8ab8830bc4501d21fe6868)

BibTeX

@misc{dc315e6bb7b024,
  title = {Beyond Tables: Doc2DB-Bench for Relationally Faithful Document-to-Database Construction — arXiv:2608.08459},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.08459},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_315e6bb7b024; sha256 91bca926e7459a6c6a23b5166968be135f74be120e8ab8830bc4501d21fe6868},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.08459. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Practical AI systems increasingly need to turn long, heterogeneous documents into queryable relational databases, not isolated spreadsheets. In domains such as finance, healthcare, education, transportation, and enterprise operations, downstream workflows rely on normalized schemas, entity identities, keys, cross-table relationships, and integrity constraints for analytics, compliance, auditing, and SQL-backed decision making. Existing Document-to-Table benchmarks are insufficient for this setting: flattening evidence into single tables can duplicate entities, obscure many-to-many relationships, create sparse records, and avoid testing whether extracted facts form a valid database instance. This creates an urgent need to evaluate document understanding as database construction rather than field extraction. We introduce Doc2DB-Bench, a benchmark for Document-to-Database construction, containing 203 long-document instances across 42 schemas and seven domain groups, with 117 entity tables, 132 relationship tables, 7,341 rows, and 41,935 cells. Built through a controllable DB-to-Doc synthesis pipeline and organized by a taxonomy of intra-table extraction and inter-table reasoning, the generated documents undergo authenticity verification, proving indistinguishable from real-world references. Doc2DB-Bench thus provides a testbed for reliable, auditable, and relationally faithful LLM-based data systems. The benchmark is publicly available at this https URL .

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-11 in cs.CL).
Categorie arXiv: Computation and Language (cs.CL) ; Artificial Intelligence (cs.AI); Databases (cs.DB).
Pagina del lavoro: https://arxiv.org/abs/2608.08459 — 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 2026-10-19T08:49:41.859Z dalla pagina https://arxiv.org/catchup/cs.CL/2026-08-11?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).