FUN with Fisher: Improving Generalization of Adapter-Based Cross-lingual Transfer with Scheduled Unfreezing (2024.naacl-long)
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| Challenge: | Standard fine-tuning of language models suffers with generalization to distribution shifts. |
| Approach: | They propose to use Fisher Information to investigate scheduled unfreezing algorithms for adapter-based cross-lingual task transfer to improve generalization to distribution shifts. |
| Outcome: | The proposed method achieves an average of 2 points improvement over four datasets compared to standard fine-tuning and provides empirical evidence for a theory-based justification of the proposed method. |
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