Papers by Xiayang Shi

1 papers
Enhancing Translation Ability of Large Language Models by Leveraging Task-Related Layers (2024.lrec-main)

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Challenge: Experimental validation shows that adjusting task-related layers significantly improves performance on translation tasks while maintaining stability and accuracy on other tasks.
Approach: They propose to adjust task-related layers in large models to better harness their machine translation capabilities by revealing the structure and characteristics of attention weights through singular value decomposition.
Outcome: The proposed method reduces computational resource consumption and catastrophic forgetting while maintaining stability and accuracy on other tasks.

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