Towards real-world fact-checking with large language models
13Misinformation poses a growing threat to our society. It has a severe impact on public health by promoting fake cures fear and distrust. Current research on n or vaccine hesitancy, and it is used as a weapon during military conflicts to spread natural language processing (NLP) for fact-checking focuses on identifying evidence and predicting the veracity of a claim. People's beliefs, however, often do not depend on the claim and rational reasoning but on credible content that makes the claim seem more reliable, such as scientific publications or visual content that was manipulated or stems from unrelated contexts.
In this talk, Professor Gurevych zooms in on two critical aspects of such misinformation supported by credible though misleading content. Firstly, she presents efforts to dismantle misleading narratives based on fallacious interpretations of scientific publications. Secondly, she shows how we can use multimodal large language models to (1) detect misinformation based on visual content and (2) provide strong alternative explanations for the visual content.
Speakers:
Iryna Gurevych
Professor, University of Darmstadt
Moderators:
Conrad Philipp
Senior Project Coordinator, Friedrich Schiller University Jena
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The views and opinions expressed are those of the panelists and do not reflect the official policy of the ITU.