Papers by Sungchul Choi
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. |
Pseudo Outlier Exposure for Out-of-Distribution Detection using Pretrained Transformers (2023.findings-acl)
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| Challenge: | Existing methods to detect out-of-distribution (OOD) samples are overconfident for real-world language applications. |
| Approach: | They propose a method that constructs a surrogate OOD dataset by sequentially masking tokens related to ID classes. |
| Outcome: | The proposed method can train a rejection network with ID and diverse outlier samples but requires additional data collection overhead. |