Papers by Wan Xianlin
Correcting Language Model Bias for Text Classification in True Zero-Shot Learning (2024.lrec-main)
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| Challenge: | Experimental results show that pre-trained language models outperform standard prompt learning in zero-shot settings. |
| Approach: | They propose a pipeline for annotating and filtering examples from unlabeled examples . they propose 'model bias validation' method that utilizes unlabed examples as validation set . |
| Outcome: | The proposed approach outperforms standard prompt learning on six text classification tasks. |