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arxiv: 2106.05707 · v3 · pith:WVGWET7Tnew · submitted 2021-06-10 · 💻 cs.CL

FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured information

Rami Aly , Zhijiang Guo , Michael Schlichtkrull , James Thorne , Andreas Vlachos , Christos Christodoulopoulos , Oana Cocarascu , Arpit Mittal This is my paper
classification 💻 cs.CL
keywords informationevidenceclaimsfactstructuredtablesunstructuredverification
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Fact verification has attracted a lot of attention in the machine learning and natural language processing communities, as it is one of the key methods for detecting misinformation. Existing large-scale benchmarks for this task have focused mostly on textual sources, i.e. unstructured information, and thus ignored the wealth of information available in structured formats, such as tables. In this paper we introduce a novel dataset and benchmark, Fact Extraction and VERification Over Unstructured and Structured information (FEVEROUS), which consists of 87,026 verified claims. Each claim is annotated with evidence in the form of sentences and/or cells from tables in Wikipedia, as well as a label indicating whether this evidence supports, refutes, or does not provide enough information to reach a verdict. Furthermore, we detail our efforts to track and minimize the biases present in the dataset and could be exploited by models, e.g. being able to predict the label without using evidence. Finally, we develop a baseline for verifying claims against text and tables which predicts both the correct evidence and verdict for 18% of the claims.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. TSVer: A Benchmark for Fact Verification Against Time-Series Evidence

    cs.CL 2025-11 unverdicted novelty 7.0

    TSVer is a new benchmark dataset for fact verification against time-series evidence, with 304 annotated real-world claims, 400 time series, verdicts, and justifications, plus baseline results showing current models struggle.

  2. From Articles to Premises: Building PrimeFacts, an Extraction Methodology and Resource for Fact-Checking Evidence

    cs.CL 2026-05 unverdicted novelty 6.0

    PrimeFacts extracts decontextualized premises from fact-check articles, raising evidence retrieval MRR by up to 30% and verdict prediction Macro-F1 by 10-20 points over baselines.

  3. Althea: Human-AI Collaboration for Fact-Checking and Critical Reasoning

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    Althea integrates retrieval-augmented reasoning with varying levels of user scaffolding to improve fact-checking accuracy and foster persistent improvements in critical thinking.