Papers with combat misinformation
On the Risk of Misinformation Pollution with Large Language Models (2023.findings-emnlp)
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| Challenge: | a recent study demonstrates that large language models can be misused for generating credible-sounding misinformation . however, the ability to produce credible text raises concerns regarding their potential misuse . |
| Approach: | They propose three defense strategies to mitigate misinformation generated by Large Language Models . they propose a threat model and simulate potential misuse scenarios . |
| Outcome: | The proposed defense strategies have shown promising results, albeit with costs. |
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. |
Towards Reliable Misinformation Mitigation: Generalization, Uncertainty, and GPT-4 (2023.emnlp-main)
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Kellin Pelrine, Anne Imouza, Camille Thibault, Meilina Reksoprodjo, Caleb Gupta, Joel Christoph, Jean-François Godbout, Reihaneh Rabbany
| Challenge: | Misinformation is a critical societal challenge, and current approaches have yet to produce an effective solution. |
| Approach: | They propose to focus on generalization, uncertainty and how to leverage large language models . they propose techniques to handle uncertainty that can detect impossible examples and strongly improve outcomes . |
| Outcome: | The proposed tools outperform previous methods in multiple settings and languages. |