Papers with perturbation perplexity
From Fake to Real: Mitigating Out-of-Distribution Bias in In-Context Learning via Feedback Supervision from Large Language Models (2026.findings-acl)
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| Challenge: | In-Context Learning (ICL) is one of the most common methods for complex Natural Language Understanding tasks. |
| Approach: | They propose a method that uses model confidence and perturbation perplexity to enhance the quality of pseudo-labels. |
| Outcome: | The proposed method reduces OOD biases by avoiding direct use of source data. |