Papers with fact-checking process
PubHealthTab: A Public Health Table-based Dataset for Evidence-based Fact Checking (2022.findings-naacl)
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| Challenge: | Fact-checking is the task of establishing the veracity of factual information, commonly performed manually by journalists. |
| Approach: | They propose a table fact-checking dataset based on real world public health claims and noisy evidence tables from sources similar to those used by fact checkers. |
| Outcome: | The proposed dataset achieves an overall F1 score of 0.73 . |
FAKTA: An Automatic End-to-End Fact Checking System (N19-4)
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| Challenge: | Existing studies have investigated individual components of fact checking process but none offer such a capability. |
| Approach: | They propose a framework that integrates various components of a fact-checking process. |
| Outcome: | The proposed framework integrates various components of a fact-checking process to predict the factuality of claims and provide evidence at the document and sentence level to explain its predictions. |
MultiCW: A Large-Scale Balanced Benchmark Dataset for Training Robust Check-Worthiness Detection Models (2026.findings-eacl)
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| Challenge: | Large language models (LLMs) are beginning to reshape how media professionals verify information, but support for detecting check-worthy claims remains limited. |
| Approach: | They propose a multilingual benchmark for check-worthy claim detection spanning 16 languages, six topical domains, and two writing styles. |
| Outcome: | The proposed model outperforms zero-shot LLMs on claim classification and strong generalization across languages, domains, and styles. |
Verify-in-the-Graph: Entity Disambiguation Enhancement for Complex Claim Verification with Interactive Graph Representation (2025.naacl-long)
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| Challenge: | Existing approaches to claim verification are based on decomposing claims into sub-claims and querying a knowledge base to resolve hidden or ambiguous entities. |
| Approach: | They propose a framework that leverages the reasoning and comprehension abilities of LLM agents to solve ambiguous entities in a graph. |
| Outcome: | The proposed framework achieves competitive performance compared to baselines across benchmarks. |