Papers with calibration accuracy

2 papers
Bag of Tricks for In-Distribution Calibration of Pretrained Transformers (2023.findings-eacl)

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Challenge: Recent studies show that pre-trained language models (PLMs) often predict over-confidently.
Approach: They propose to use ensemble learning and data augmentation to improve confidence calibration for PLMs by combining calibration techniques with a trade-off between accuracy and classification.
Outcome: The proposed calibration method improves classification accuracy and confidence in pre-trained language models by combining several calibration techniques.
Knowing More About Questions Can Help: Improving Calibration in Question Answering (2021.findings-acl)

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Challenge: Existing work on calibration focuses on model confidence, such as the max probability of the predicted class.
Approach: They propose a calibration method which estimates whether model correctly predicts answer for each question.
Outcome: The proposed calibration method achieves 5-10% gains on reading comprehension benchmarks.

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