On Subjective Uncertainty Quantification and Calibration in Natural Language Generation
This work addresses uncertainty quantification for users of large language models in tasks like question answering and machine translation, offering incremental improvements in calibration and deferral strategies.
The paper tackles the challenge of quantifying and calibrating uncertainty in natural language generation by proposing a Bayesian decision theory approach based on similarity measures, enabling principled evaluation and demonstrating that epistemic uncertainty can improve deferral strategies for data acquisition in in-context learning.
Applications of large language models often involve the generation of free-form responses, in which case uncertainty quantification becomes challenging. This is due to the need to identify task-specific uncertainties (e.g., about the semantics) which appears difficult to define in general cases. This work addresses these challenges from a perspective of Bayesian decision theory, starting from the assumption that our utility is characterized by a similarity measure that compares a generated response with a hypothetical true response. We discuss how this assumption enables principled quantification of the model's subjective uncertainty and its calibration. We further derive a measure for epistemic uncertainty, based on a missing data perspective and its characterization as an excess risk. The proposed methods can be applied to black-box language models. We illustrate the methods on question answering and machine translation tasks. Our experiments provide a principled evaluation of task-specific calibration, and demonstrate that epistemic uncertainty offers a promising deferral strategy for efficient data acquisition in in-context learning.