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ClaimDB: A Fact Verification Benchmark over Large Structured Data

Paper · 2026 · Michael Theologitis, Preetam Prabhu Srikar Dammu, Chirag Shah et al.

Summary

Many consequential public claims, such as statements about inflation or crime rates, can only be checked against large official datasets. Existing fact-checking benchmarks rarely test this, because their evidence (Wikipedia tables, short texts, small relational tables) fits inside an LLM's context window. ClaimDB fills this gap. Each claim must be judged as entailed, contradicted, or not-enough-info (NEI) against a real multi-table database.

The authors built it from the BIRD NL2SQL benchmark in four steps:

  • They ran each SQL query on its database.
  • They used the query's AST to keep only pairs that require reasoning over large portions of the data.
  • They had gpt-5 generate claims from the gold answers.
  • They filtered the claims with a panel of LLM judges.

The evidence per claim is far too large to read directly, so verification has to rely on executable programs. The authors tested 30 LLMs as agents that can run SQL. More than half scored below 55% accuracy, and both closed and open models handled abstention poorly. This raises concerns about using LLMs for high-stakes data analysis.

Key points

  • Scale: 53,368 claims (12,855 entailed, 16,529 contradicted, 23,984 NEI) over 80 real BIRD databases in 11 domains. Each database averages about 11.3 tables, 4.6M records, and roughly 110M tokens of evidence.
  • NEI subtypes: out-of-schema (12,644), counterfactual (5,786), and subjective (5,554).
  • Pipeline: the AST filter kept queries with ordering/superlatives, aggregates, or window functions. The judges came from Mistral AI, Microsoft, and xAI, and they reduced 62,018 generated claims to the final set. Test splits come from BIRD's cleaner dev split.
  • Abstention: proprietary models (gpt-5-mini, claude-haiku-4.5) almost never choose NEI, while open models (qwen3, nemotron-3-nano) choose it far too often. This NEI behaviour explains most of the closed/open performance gap.
  • Availability: the benchmark, code, and leaderboard are released at claimdb.github.io under CC BY-SA 4.0.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓University of WashingtonSeattle · United States353 works · 200,043 citations

 Fields, topics and concepts

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksComputer Vision and Pattern Recognition · 2,869 worksSocial Sciences · 1,772 worksSociology and Political Science · 577 works

🧭 Topics 1Research subjects and languages the work is about.Evaluation & benchmarks · 1,220 works

Cite

Cite as Michael Theologitis, Preetam Prabhu Srikar Dammu, Chirag Shah and Dan Suciu. 2026. ClaimDB: A Fact Verification Benchmark over Large Structured Data. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-long.1589

@inproceedings{theologitis2026claimdb,
  title = {{ClaimDB: A Fact Verification Benchmark over Large Structured Data}},
  author = {Michael Theologitis, Preetam Prabhu Srikar Dammu, Chirag Shah and Dan Suciu},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1589},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1589}
}

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