6 papers
The Heterogeneous Safety Impacts of Benign Multilingual Fine-Tuning
Will Hawkins, Kaivalya Rawal, Jonathan Rystrøm +8
Fine-tuning a large language model is a ubiquitous method for enhancing its capability on a specific downstream task. However, prior work has shown that this increase in capability…
Explaining Sources of Uncertainty in Automated Fact-Checking
Jingyi Sun, Greta Warren, Irina Shklovski +1
Understanding sources of a model's uncertainty regarding its predictions is crucial for effective human-AI collaboration. Prior work proposes using numerical uncertainty or hedges…
Show me the evidence: Evaluating the role of evidence and natural language explanations in AI-supported fact-checking
Greta Warren, Jingyi Sun, Irina Shklovski +1
Although much research has focused on AI explanations to support decisions in complex information-seeking tasks such as fact-checking, the role of evidence is surprisingly under-re…
Can Community Notes Replace Professional Fact-Checkers?
Nadav Borenstein, Greta Warren, Desmond Elliott +1
Two commonly employed strategies to combat the rise of misinformation on social media are (i) fact-checking by professional organisations and (ii) community moderation by platform…
Community Moderation and the New Epistemology of Fact Checking on Social Media
Isabelle Augenstein, Michiel Bakker, Tanmoy Chakraborty +13
Social media platforms have traditionally relied on internal moderation teams and partnerships with independent fact-checking organizations to identify and flag misleading content.…
Show Me the Work: Fact-Checkers' Requirements for Explainable Automated Fact-Checking
Greta Warren, Irina Shklovski, Isabelle Augenstein
The pervasiveness of large language models and generative AI in online media has amplified the need for effective automated fact-checking to assist fact-checkers in tackling the in…