Misinformation such as fake news and rumors is a serious threat for information ecosystems
and public trust. The emergence of large language models (LLMs) has great potential to …

Internet and social media have revolutionised the way news is distributed and consumed.
However, the constant flow of massive amounts of content has made it difficult to discern …

Fact-checking has become increasingly important due to the speed with which both
information and misinformation can spread in the modern media ecosystem. Therefore …

Existing datasets for automated fact-checking have substantial limitations, such as relying on
artificial claims, lacking annotations for evidence and intermediate reasoning, or including …

Fact-checking real-world claims often requires collecting multiple pieces of evidence and
applying complex multi-step reasoning. In this paper, we present Program-Guided Fact …

Fact verification has attracted a lot of attention in the machine learning and natural language
processing communities, as it is one of the key methods for detecting misinformation …

Misinformation threatens modern society by promoting distrust in science, changing
narratives in public health, heightening social polarization, and disrupting democratic …

Recent advances in the capacity of large language models to generate human-like text have
resulted in their increased adoption in user-facing settings. In parallel, these improvements …

Large language models (LMs) are prone to generate factual errors, which are often called
hallucinations. In this paper, we introduce a comprehensive taxonomy of hallucinations and …

While human evaluation remains best practice for accurately judging the faithfulness of
automatically-generated summaries, few solutions exist to address the increased difficulty …