Assessing the Portability of Parameter Matrices Trained by Parameter-Efficient Finetuning Methods (2024.findings-eacl)
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| Challenge: | Transfer learning methods have shown that reusing non-task-specific knowledge can speed up task-specific learning in the latter. |
| Approach: | They propose to port whole functional modules that encode task-specific knowledge from one model to another using parameter-efficient finetuning techniques. |
| Outcome: | The proposed methods outperform the two alternatives, but there are differences between the two. |
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