  Misinformation poses a critical societal challenge, and current approaches
have yet to produce an effective solution. We propose focusing on
generalization, uncertainty, and how to leverage recent large language models,
in order to create more practical tools to evaluate information veracity in
contexts where perfect classification is impossible. We first demonstrate that
GPT-4 can outperform prior methods in multiple settings and languages. Next, we
explore generalization, revealing that GPT-4 and RoBERTa-large exhibit
differences in failure modes. Third, we propose techniques to handle
uncertainty that can detect impossible examples and strongly improve outcomes.
We also discuss results on other language models, temperature, prompting,
versioning, explainability, and web retrieval, each one providing practical
insights and directions for future research. Finally, we publish the LIAR-New
dataset with novel paired English and French misinformation data and
Possibility labels that indicate if there is sufficient context for veracity
evaluation. Overall, this research lays the groundwork for future tools that
can drive real-world progress to combat misinformation.
