Papers by Neal Lawton
Neural Architecture Search for Parameter-Efficient Fine-tuning of Large Pre-trained Language Models (2023.findings-acl)
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| Challenge: | Parameter-efficient tuning (PET) methods fit pre-trained language models to downstream tasks by computing a small compressed update for a subset of parameter updates or appending and fine-tuning a few new parameters to the pre-train network. |
| Approach: | They propose a parameter-efficient tuning method that prunes PET architectures using structured pruning. |
| Outcome: | The proposed method performs well in practice but could be improved via NAS . it can be used to fine-tune large pre-trained language models on multiple tasks . |