Finetuning Language Models to Emit Linguistic Expressions of Uncertainty
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| Format: | Preprint |
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2024
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| _version_ | 1866929504264388608 |
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| author | Chaudhry, Arslan Thiagarajan, Sridhar Gorur, Dilan |
| author_facet | Chaudhry, Arslan Thiagarajan, Sridhar Gorur, Dilan |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2409_12180 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Finetuning Language Models to Emit Linguistic Expressions of Uncertainty Chaudhry, Arslan Thiagarajan, Sridhar Gorur, Dilan Computation and Language Machine Learning 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. |
| title | Finetuning Language Models to Emit Linguistic Expressions of Uncertainty |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2409.12180 |