Uncertainty Estimation and Reduction of Pre-trained Models for Text Regression (2022.tacl-1)
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| Challenge: | State-of-the-art classification and regression models are often not well calibrated and can be inaccurate. |
| Approach: | They quantify calibration of pre- trained language models for text regression . they apply uncertainty estimates to augment training data in low-resource domains . |
| Outcome: | The proposed model calibrations improve performance and generalizability in low-resource settings. |
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