Dylan Bouchard, Mohit Singh Chauhan, Viren Bajaj, David Skarbrevik
This study introduces a taxonomy for fine-grained uncertainty quantification in long-form language model outputs, revealing that claim-level scoring and uncertainty-aware decoding improve factuality in generated content.
The paper focuses on improving how we measure uncertainty in the outputs of language models, specifically when they generate long-form content, like essays or articles. The authors propose a new way to categorize methods for assessing uncertainty, which helps to detect when a language model might be 'hallucinating' or producing incorrect information. They find that evaluating the factuality at the level of entire claims (rather than individual sentences) and using uncertainty-aware methods to guide the model's output can significantly enhance the accuracy of the generated text. This research helps us better understand and improve the reliability of AI-generated long-form content.
