ETHICIST: Targeted Training Data Extraction Through Loss Smoothed Soft Prompting and Calibrated Confidence Estimation (2023.acl-long)
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| Challenge: | Recent studies show that pre-trained language models memorize a considerable fraction of training data, leading to privacy risk of information leakage. |
| Approach: | They propose a method for targeted training data extraction using a smoothed soft prompting and calibrated confidence estimation. |
| Outcome: | The proposed method significantly improves the extraction performance on a recently proposed public benchmark. |
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