ClaimDB: A Fact Verification Benchmark over Large Structured Data
Michael Theologitis, Preetam Prabhu Srikar Dammu, Chirag Shah, Dan Suciu

TL;DR
ClaimDB introduces a large-scale fact verification benchmark over structured data, highlighting the challenges for current models in reasoning and abstention, and providing a new resource for advancing fact-checking research.
Contribution
This work presents ClaimDB, a comprehensive benchmark with real-world databases for evaluating fact verification methods over structured data, and analyzes the performance of state-of-the-art LLMs.
Findings
Over half of the models score below 55% accuracy.
Models struggle with abstention, admitting lack of evidence.
The benchmark and leaderboard are publicly released.
Abstract
Real-world fact-checking often involves verifying claims grounded in structured data at scale. Despite substantial progress in fact-verification benchmarks, this setting remains largely underexplored. In this work, we introduce ClaimDB, a fact-verification benchmark where the evidence for claims is derived from compositions of millions of records and multiple tables. ClaimDB consists of 80 unique real-life databases covering a wide range of domains, from governance and healthcare to media, education and the natural sciences. At this scale, verification approaches that rely on "reading" the evidence break down, forcing a timely shift toward reasoning in executable programs. We conduct extensive experiments with 30 state-of-the-art proprietary and open-source (below 70B) LLMs and find that more than half score below 55% accuracy. Our analysis also reveals that both closed- and open-source…
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