QuanTemp: A real-world open-domain benchmark for fact-checking numerical claims

• URL: http://arxiv.org/abs/2403.17169v3
• Date: Wed, 1 May 2024 06:27:24 GMT
• Title: QuanTemp: A real-world open-domain benchmark for fact-checking numerical claims
• Abstract summary: We release QuanTemp, a dataset focused exclusively on numerical claims. We evaluate and quantify the limitations of existing solutions for the task of verifying numerical claims.
• Score: 4.874071145951159
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automated fact checking has gained immense interest to tackle the growing misinformation in the digital era. Existing systems primarily focus on synthetic claims on Wikipedia, and noteworthy progress has also been made on real-world claims. In this work, we release QuanTemp, a diverse, multi-domain dataset focused exclusively on numerical claims, encompassing temporal, statistical and diverse aspects with fine-grained metadata and an evidence collection without leakage. This addresses the challenge of verifying real-world numerical claims, which are complex and often lack precise information, not addressed by existing works that mainly focus on synthetic claims. We evaluate and quantify the limitations of existing solutions for the task of verifying numerical claims. We also evaluate claim decomposition based methods, numerical understanding based models and our best baselines achieves a macro-F1 of 58.32. This demonstrates that QuanTemp serves as a challenging evaluation set for numerical claim verification.

Related papers

• TSVer: A Benchmark for Fact Verification Against Time-Series Evidence [8.095827820420839]
  We introduce TSVer, a new benchmark dataset for fact verification focusing on temporal and numerical reasoning with time-series evidence.<n> TSVer contains 287 real-world claims sourced from 38 fact-checking organizations and a curated database of 400 time series covering diverse domains.
  arXiv  Detail & Related papers  (2025-11-02T22:33:19Z)
• A Benchmark for Open-Domain Numerical Fact-Checking Enhanced by Claim Decomposition [7.910984819642885]
  QuanTemp++ is a dataset consisting of natural numerical claims, an open domain corpus, with the corresponding relevant evidence for each claim.<n>We characterize the retrieval performance of key claim decomposition paradigms.
  arXiv  Detail & Related papers  (2025-10-24T22:37:13Z)
• Demystifying deep search: a holistic evaluation with hint-free multi-hop questions and factorised metrics [89.1999907891494]
  We present WebDetective, a benchmark of hint-free multi-hop questions paired with a controlled Wikipedia sandbox.<n>Our evaluation of 25 state-of-the-art models reveals systematic weaknesses across all architectures.<n>We develop an agentic workflow, EvidenceLoop, that explicitly targets the challenges our benchmark identifies.
  arXiv  Detail & Related papers  (2025-10-01T07:59:03Z)
• FactIR: A Real-World Zero-shot Open-Domain Retrieval Benchmark for Fact-Checking [3.1537425078180625]
  The field of automated fact-checking increasingly depends on retrieving web-based evidence to determine the veracity of claims in real-world scenarios. Traditional retrieval methods may return documents that directly address claims or lean toward supporting them, but often struggle with more complex claims requiring indirect reasoning. We present a real-world benchmark FactIR, derived from Factiverse production logs, enhanced with human annotations.
  arXiv  Detail & Related papers  (2025-02-09T19:51:00Z)
• FactLens: Benchmarking Fine-Grained Fact Verification [6.814173254027381]
  We advocate for a shift toward fine-grained verification, where complex claims are broken down into smaller sub-claims for individual verification. We introduce FactLens, a benchmark for evaluating fine-grained fact verification, with metrics and automated evaluators of sub-claim quality. Our results show alignment between automated FactLens evaluators and human judgments, and we discuss the impact of sub-claim characteristics on the overall verification performance.
  arXiv  Detail & Related papers  (2024-11-08T21:26:57Z)
• ChronoFact: Timeline-based Temporal Fact Verification [15.698391632115856]
  Temporal claims, often riddled with inaccuracies, are a significant challenge in the digital misinformation landscape.<n>We introduce a novel timeline-based fact verification framework that identifies events from both claim and evidence.<n>We also introduce a new dataset of complex temporal claims involving timeline-based reasoning.
  arXiv  Detail & Related papers  (2024-10-19T03:44:19Z)
• Contrastive Learning to Improve Retrieval for Real-world Fact Checking [84.57583869042791]
  We present Contrastive Fact-Checking Reranker (CFR), an improved retriever for fact-checking complex claims. We leverage the AVeriTeC dataset, which annotates subquestions for claims with human written answers from evidence documents. We find a 6% improvement in veracity classification accuracy on the dataset.
  arXiv  Detail & Related papers  (2024-10-07T00:09:50Z)
• OpenFactCheck: Building, Benchmarking Customized Fact-Checking Systems and Evaluating the Factuality of Claims and LLMs [59.836774258359945]
  OpenFactCheck is a framework for building customized automatic fact-checking systems.<n>It allows users to easily customize an automatic fact-checker and verify the factual correctness of documents and claims.<n>CheckerEVAL is a solution for gauging the reliability of automatic fact-checkers' verification results using human-annotated datasets.
  arXiv  Detail & Related papers  (2024-05-09T07:15:19Z)
• From Chaos to Clarity: Claim Normalization to Empower Fact-Checking [57.024192702939736]
  Claim Normalization (aka ClaimNorm) aims to decompose complex and noisy social media posts into more straightforward and understandable forms. We propose CACN, a pioneering approach that leverages chain-of-thought and claim check-worthiness estimation. Our experiments demonstrate that CACN outperforms several baselines across various evaluation measures.
  arXiv  Detail & Related papers  (2023-10-22T16:07:06Z)
• AVeriTeC: A Dataset for Real-world Claim Verification with Evidence from the Web [20.576644330553744]
  We introduce AVeriTeC, a new dataset of 4,568 real-world claims covering fact-checks by 50 different organizations. Each claim is annotated with question-answer pairs supported by evidence available online, as well as textual justifications explaining how the evidence combines to produce a verdict.
  arXiv  Detail & Related papers  (2023-05-22T15:17:18Z)
• WiCE: Real-World Entailment for Claims in Wikipedia [63.234352061821625]
  We propose WiCE, a new fine-grained textual entailment dataset built on natural claim and evidence pairs extracted from Wikipedia. In addition to standard claim-level entailment, WiCE provides entailment judgments over sub-sentence units of the claim. We show that real claims in our dataset involve challenging verification and retrieval problems that existing models fail to address.
  arXiv  Detail & Related papers  (2023-03-02T17:45:32Z)
• Generating Literal and Implied Subquestions to Fact-check Complex Claims [64.81832149826035]
  We focus on decomposing a complex claim into a comprehensive set of yes-no subquestions whose answers influence the veracity of the claim. We present ClaimDecomp, a dataset of decompositions for over 1000 claims. We show that these subquestions can help identify relevant evidence to fact-check the full claim and derive the veracity through their answers.
  arXiv  Detail & Related papers  (2022-05-14T00:40:57Z)
• Synthetic Disinformation Attacks on Automated Fact Verification Systems [53.011635547834025]
  We explore the sensitivity of automated fact-checkers to synthetic adversarial evidence in two simulated settings. We show that these systems suffer significant performance drops against these attacks. We discuss the growing threat of modern NLG systems as generators of disinformation.
  arXiv  Detail & Related papers  (2022-02-18T19:01:01Z)
• FaVIQ: FAct Verification from Information-seeking Questions [77.7067957445298]
  We construct a large-scale fact verification dataset called FaVIQ using information-seeking questions posed by real users. Our claims are verified to be natural, contain little lexical bias, and require a complete understanding of the evidence for verification.
  arXiv  Detail & Related papers  (2021-07-05T17:31:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.