An Empirical Study on the Transferability of Transformer Modules in Parameter-efficient Fine-tuning (2022.emnlp-main)
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| Challenge: | Parameter-efficient fine-tuning is a computationally expensive process . introducing new parameters to an already-large model can be considered a drawback. |
| Approach: | They investigate the capability of different transformer modules in transferring knowledge from a pre-trained model to a downstream task. |
| Outcome: | The proposed methods show that each transformer module is a winning ticket . they show that with only 0.003% updateable parameters, they can show acceptable performance on target tasks. |
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| Challenge: | State-of-the-art language models in NLP perform best when fine-tuned even on small datasets. |
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| Challenge: | Existing methods to fine-tune large language models primarily focus on the interaction between different layers, ignoring the fact that different layers store different information. |
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