Papers with generalization of adapter-based cross-lingual task transfer

1 papers
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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