Papers with fact-checking approaches
CONCRETE: Improving Cross-lingual Fact-checking with Cross-lingual Retrieval (2022.coling-1)
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| Challenge: | Existing fact-checking approaches focus on claims made in English due to data scarcity issue in other languages. |
| Approach: | They propose a fact-checking framework augmented with cross-lingual retrieval that aggregates evidence retrieved from multiple languages through a cross-linguistic retriever. |
| Outcome: | The proposed framework achieves 2.23% absolute F1 improvement over previous systems on a X-Fact dataset. |
The Psychology of Falsehood: A Human-Centric Survey of Misinformation Detection (2025.emnlp-main)
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Arghodeep Nandi, Megha Sundriyal, Euna Mehnaz Khan, Jikai Sun, Emily K. Vraga, Jaideep Srivastava, Tanmoy Chakraborty
| Challenge: | a survey examines the interplay between factual accuracy and cognitive biases . misinformation is more than just the existence of incorrect information, it also entails complex relationships between the information and the entities that consume it. |
| Approach: | They examine the interplay between traditional fact-checking and psychological concepts such as cognitive biases, social dynamics, and emotional responses. |
| Outcome: | The findings highlight limitations of current methods and identify opportunities for improvement . they also outline future research directions to create more robust frameworks . |
SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models (2023.emnlp-main)
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| Challenge: | Existing fact-checking approaches require access to external databases or external databases . a lack of external databases can undermine trust in large language models. |
| Approach: | They propose a sampling-based approach to fact-check black-box models without external databases. |
| Outcome: | The proposed approach can be used to fact-check black-box models without external databases . it can detect non-factual and factual sentences and rank passages in terms of factuality . |
ClaimVer: Explainable Claim-Level Verification and Evidence Attribution of Text Through Knowledge Graphs (2024.findings-emnlp)
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Preetam Prabhu Srikar Dammu, Himanshu Naidu, Mouly Dewan, YoungMin Kim, Tanya Roosta, Aman Chadha, Chirag Shah
| Challenge: | Despite the fact that many fact-checking tools lack granularity and explainability, they lack the ability to be useful in various contexts. |
| Approach: | They propose a text validation framework that provides granular explanations for each claim and localizes the specific problematic content to reduce cognitive load. |
| Outcome: | The proposed framework provides granular explanations for each claim prediction and localizes and educates users on the specific content. |