Papers with fact-checking pipeline
CoVERT: A Corpus of Fact-checked Biomedical COVID-19 Tweets (2022.lrec-1)
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| Challenge: | Existing fact-checking resources cover COVID-19 related information in news, but there is no dataset providing fact- checked COVId-19 related tweets with detailed annotations for biomedical entities, relations and relevant evidence. |
| Approach: | They propose a fact-checked corpus of tweets with annotations for biomedical entities, relations and relevant evidence for COVID-19 related tweets. |
| Outcome: | The proposed dataset provides fact-checked COVID-19 related tweets with detailed annotations for biomedical entities, relations and relevant evidence. |
AFaCTA: Assisting the Annotation of Factual Claim Detection with Reliable LLM Annotators (2024.acl-long)
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| Challenge: | generative AI is a counter-measure to misinformation, but factual claim detection suffers from inconsistency in definitions and high cost of manual annotation. |
| Approach: | They propose a framework that assists in the annotation of factual claims with the help of large language models. |
| Outcome: | The proposed framework can be used to annotate factual claims with the help of large language models and can work with or without expert supervision. |
Generating Literal and Implied Subquestions to Fact-check Complex Claims (2022.emnlp-main)
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| Challenge: | Existing fact-checking systems are not reliable because it is unclear which parts of a claim are true and which are not. |
| Approach: | They propose to decompose a political claim into a comprehensive set of yes-no subquestions whose answers influence the veracity of the claim. |
| Outcome: | The proposed models can decompose a complex claim into a comprehensive set of yes-no subquestions whose answers influence the veracity of the claim. |
Explainable Automated Fact-Checking: A Survey (2020.coling-main)
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| Challenge: | Steady progress has been made in fact-checking and its orthogonal tasks. |
| Approach: | They propose to use fact-checking explanations to explain predictions by comparing existing explanations against desirable properties to find out what makes for good explanations. |
| Outcome: | The proposed explanations are compared against desirable properties and show how they may lead to improvements in the research area. |
Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification (2024.findings-acl)
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Ekaterina Fadeeva, Aleksandr Rubashevskii, Artem Shelmanov, Sergey Petrakov, Haonan Li, Hamdy Mubarak, Evgenii Tsymbalov, Gleb Kuzmin, Alexander Panchenko, Timothy Baldwin, Preslav Nakov, Maxim Panov
| Challenge: | Large language models are notorious for producing erroneous claims in their output. |
| Approach: | They propose a fact-checking and hallucination detection pipeline based on token-level uncertainty quantification that removes the impact of uncertainty about what claim to generate on the current step and what surface form to use. |
| Outcome: | The proposed method can fact-check the atomic claims in the output of large language models. |