On Subjective Uncertainty Quantification and Calibration in Natural Language Generation

• URL: http://arxiv.org/abs/2406.05213v2
• Date: Fri, 18 Oct 2024 02:55:27 GMT
• Title: On Subjective Uncertainty Quantification and Calibration in Natural Language Generation
• Authors: Ziyu Wang, Chris Holmes,
• Abstract summary: Large language models often involve the generation of free-form responses, in which case uncertainty quantification becomes challenging. This work addresses these challenges from a perspective of Bayesian decision theory. We discuss how this assumption enables principled quantification of the model's subjective uncertainty and its calibration. The proposed methods can be applied to black-box language models.
• Score: 2.622066970118316
• License:
• Abstract: 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.

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