QuanTemp: A real-world open-domain benchmark for fact-checking numerical claims
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Abstract (arXiv)
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.
Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). Builds on 33 of its 58 references that are in the map. First posted on arXiv 25 Mar 2024.
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33 of 58 references.
References · 33
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Citing papers per year: 2024: 4; 2025: 11; 2026: 7.
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- 2026Towards Robust Numerical Claim Verification30 Sep 2026
- 2026SoK: Formal Methods for Fact-Checking and Information Integrity19 Sep 2026
- 2026DS@GT ARC at CheckThat! 2026: LLM-Based Trace Ranking and Grouped Reward Modeling for Multilingual Numerical Claim Verification27 Jul 2026
- 2026Diagnosing LLM Arbitration Behavior over Pre-evidence Epistemic States in RAG-based Fact-Checking31 May 2026
- 2026Symbolic Augmentation Closes a Canonical-Equivalence Blind Spot in Neural Fact-Checkers19 May 2026
- 2026The CLEF-2026 CheckThat! Lab: Advancing Multilingual Fact-Checking10 Feb 2026
- 2026One LLM to Train Them All: Multi-Task Learning Framework for Fact-Checking16 Jan 2026
- 2025NumPert: Numerical Perturbations to Probe Language Models for Veracity Prediction13 Nov 2025
- 2025TSVer: A Benchmark for Fact Verification Against Time-Series Evidence2 Nov 2025
- 2025A Benchmark for Open-Domain Numerical Fact-Checking Enhanced by Claim Decomposition24 Oct 2025
- 2025ShortCheck: Checkworthiness Detection of Multilingual Short-Form Videos24 Sep 2025
- 2025ClaimIQ at CheckThat! 2025: Comparing Prompted and Fine-Tuned Language Models for Verifying Numerical Claims15 Sep 2025
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- 2025A Study into Investigating Temporal Robustness of LLMs21 Mar 2025
- 2025The CLEF-2025 CheckThat! Lab: Subjectivity, Fact-Checking, Claim Normalization, and Retrieval19 Mar 2025
- 2025From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systems3 Mar 2025
- 2024TrendFact: A Benchmark Towards Hotspot Perception in Automatic Fact-Checking19 Oct 2024
- 2024ChronoFact: Timeline-based Temporal Fact Verification19 Oct 2024
- 2024Claim Verification in the Age of Large Language Models: A Survey26 Aug 2024
- 2024QuestGen: Effectiveness of Question Generation Methods for Fact-Checking Applications31 Jul 2024
- 2025Test-time Corpus Feedback: From Retrieval to RAGcited by 3
- 2025Test-time Corpus Feedback: From Retrieval to RAG21 Aug 2025
- 2024Claim Verification in the Age of Large Language Models: A Surveycited by 23
- 2025The CLEF-2025 CheckThat! Lab: Subjectivity, Fact-Checking, Claim Normalization, and Retrievalcited by 14
- 2025Sample Efficient Demonstration Selection for In-Context Learningcited by 12
- 2025A Study into Investigating Temporal Robustness of LLMscited by 10
- 2025From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systemscited by 8
- 2024ChronoFact: Timeline-based Temporal Fact Verificationcited by 4
- 2026The CLEF-2026 CheckThat! Lab: Advancing Multilingual Fact-Checkingcited by 3
- 2024QuestGen: Effectiveness of Question Generation Methods for Fact-Checking Applicationscited by 2
- 2025DS@GT at CheckThat! 2025: Evaluating Context and Tokenization Strategies for Numerical Fact Verificationcited by 2
- 2025ClaimIQ at CheckThat! 2025: Comparing Prompted and Fine-Tuned Language Models for Verifying Numerical Claimscited by 2
- 2025A Benchmark for Open-Domain Numerical Fact-Checking Enhanced by Claim Decompositioncited by 2
- 2025TSVer: A Benchmark for Fact Verification Against Time-Series Evidencecited by 2
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- 2026Towards Robust Numerical Claim Verification30 Sep 2026
- 2026SoK: Formal Methods for Fact-Checking and Information Integrity19 Sep 2026
- 2026Diagnosing LLM Arbitration Behavior over Pre-evidence Epistemic States in RAG-based Fact-Checking31 May 2026
- 2026Symbolic Augmentation Closes a Canonical-Equivalence Blind Spot in Neural Fact-Checkers19 May 2026
- 2026The CLEF-2026 CheckThat! Lab: Advancing Multilingual Fact-Checking10 Feb 2026
- 2026One LLM to Train Them All: Multi-Task Learning Framework for Fact-Checking16 Jan 2026
- 2025NumPert: Numerical Perturbations to Probe Language Models for Veracity Prediction13 Nov 2025
- 2025TSVer: A Benchmark for Fact Verification Against Time-Series Evidence2 Nov 2025
- 2025A Benchmark for Open-Domain Numerical Fact-Checking Enhanced by Claim Decomposition24 Oct 2025
- 2025ShortCheck: Checkworthiness Detection of Multilingual Short-Form Videos24 Sep 2025
- 2025ClaimIQ at CheckThat! 2025: Comparing Prompted and Fine-Tuned Language Models for Verifying Numerical Claims15 Sep 2025
- 2025DS@GT at CheckThat! 2025: Evaluating Context and Tokenization Strategies for Numerical Fact Verification8 Jul 2025
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