Freezing the decoder during fine-tuning maintains or improves multilingual and natural-language-generation performance, and a larger frozen decoder recovers structured and QA performance.
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A Multi-Encoder Frozen-Decoder Approach for Fine-Tuning Large Language Models
Freezing the decoder during fine-tuning maintains or improves multilingual and natural-language-generation performance, and a larger frozen decoder recovers structured and QA performance.