Uncertainty Estimation and Reduction of Pre-trained Models for Text Regression
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State-of-the-art classification and regression models are often not well calibrated, and cannot reliably provide uncertainty estimates, limiting their utility in safety-critical applications such as clinical decision-making. While recent work has focused on calibration of classifiers, there is almost no work in NLP on calibration in a regression setting. In this paper, we quantify the calibration of pre- trained language models for text regression, both intrinsically and extrinsically. We further apply uncertainty estimates to augment training data in low-resource domains. Our experiments on three regression tasks in both self-training and active-learning settings show that uncertainty estimation can be used to increase overall performance and enhance model generalization.
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Past work on uncertainty for LLMs has focused on simpler settings, such as classifiers and regressors, whereas the most exciting applications of LLMs relate to free-form generations.
“Past work on uncertainty for LLMs has focused on simpler settings, such as classifiers16,17 and regressors18,19, whereas the most exciting applications of LLMs relate to free-form generations.”
From Detecting hallucinations in large language models using semantic entropy (2024) · cites this paper as [19]
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- Authors
- Wang, Y., Beck, D., Baldwin, T., & Verspoor, K.
- Affiliations
- The University of Melbourne (AU)
- Corresponding author
- Wang, Yuxia
- Beck, Daniel
- Baldwin, Timothy
- Verspoor, Karin
- Venue
- Transactions of the Association for Computational Linguistics
- Publisher
- MIT Press
- Published
- 2022
- Volume
- 10
- Pages
- 680-696
- ISSN
- 2307-387X (ISSN-L)
- OpenAlex
- W4285298706
- Cited by
- 23
- References
- 56
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 6 · 2023: 4 · 2022: 2
- FWCI
- 1.56
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Transactions of the Association for Computational Linguistics (publishedVersion, cc-by)
- RMIT Research Repository (RMIT University Library) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion)
- Minerva Access (University of Melbourne) (submittedVersion, cc-by)
- Funders
- University of Melbourne
- China Scholarship Council
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Keywords
- uncertainty estimation, calibration, text regression, pre-trained language models, uncertainty reduction, self-training, active learning (machine learning), low-resource domains, data augmentation, model generalization
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