Finetuning Language Models to Emit Linguistic Expressions of Uncertainty

• URL: http://arxiv.org/abs/2409.12180v1
• Date: Wed, 18 Sep 2024 17:52:53 GMT
• Title: Finetuning Language Models to Emit Linguistic Expressions of Uncertainty
• Authors: Arslan Chaudhry, Sridhar Thiagarajan, Dilan Gorur,
• Abstract summary: Large language models (LLMs) are increasingly employed in information-seeking and decision-making tasks. LLMs tend to generate information that conflicts with real-world facts, and their persuasive style can make these inaccuracies appear confident and convincing. In this work, we explore supervised finetuning on uncertainty-augmented predictions as a method to develop models that produce linguistic expressions of uncertainty.
• Score: 5.591074369497796
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) are increasingly employed in information-seeking and decision-making tasks. Despite their broad utility, LLMs tend to generate information that conflicts with real-world facts, and their persuasive style can make these inaccuracies appear confident and convincing. As a result, end-users struggle to consistently align the confidence expressed by LLMs with the accuracy of their predictions, often leading to either blind trust in all outputs or a complete disregard for their reliability. In this work, we explore supervised finetuning on uncertainty-augmented predictions as a method to develop models that produce linguistic expressions of uncertainty. Specifically, we measure the calibration of pre-trained models and then fine-tune language models to generate calibrated linguistic expressions of uncertainty. Through experiments on various question-answering datasets, we demonstrate that LLMs are well-calibrated in assessing their predictions, and supervised finetuning based on the model's own confidence leads to well-calibrated expressions of uncertainty, particularly for single-claim answers.

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