Papers with detecting anomalous
Class based Influence Functions for Error Detection (2023.acl-short)
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Thang Nguyen-Duc, Hoang Thanh-Tung, Quan Hung Tran, Dang Huu-Tien, Hieu Nguyen, Anh T. V. Dau, Nghi Bui
| Challenge: | Influence functions (IFs) are powerful tools for detecting anomalous examples in large scale datasets. |
| Approach: | They propose a method to explain the instability of IFs by leveraging class information to improve the stability of ifs. |
| Outcome: | The proposed method improves performance and stability while incurring no additional computational cost. |
Pretrained Transformers Improve Out-of-Distribution Robustness (2020.acl-main)
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| Challenge: | Pretrained Transformers are more effective at detecting anomalous or OOD examples, while many previous models are frequently worse than chance. |
| Approach: | They construct a new robustness benchmark with real distribution shifts to measure out-of-distribution generalization for seven NLP datasets and compare them to previous models. |
| Outcome: | The proposed model generalizations for seven datasets show that pretrained Transformers are significantly less effective at detecting anomalous or OOD examples, while many previous models are often worse than chance. |