Year: 2026
Type: conference-paper
Abstract: Real-world fact-checking often involves verifying claims grounded in structured data at scale.Despite substantial progress in factverification 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 opensource models struggle with abstention-the ability to admit that there is no evidence to decide-raising doubts about their reliability in high-stakes data analysis tasks.We release the benchmark, code, and the LLM leaderboard at https://claimdb.github.io.BIRD Benchmark 10,962 6,545 Filter using SQL's AST . . .80 Unique DBs Execute SQL on DBs Entailed Prompt Contr.
Institution: University of Washington
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
Funder: National Science Foundation
OpenAlex ID: W7166845428