Papers by Christos Papadopoulos
Efficient Learning of Multiple NLP Tasks via Collective Weight Factorization on BERT (2022.findings-naacl)
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| Challenge: | Existing methods to fine-tune a model for multiple tasks require a large amount of memory and computing power. |
| Approach: | They propose to factorize the weighs of a pre-trained Transformer model to improve training efficiency across multiple tasks by using BERT-Large as an instantiation of the Transformer and the GLUE as the evaluation benchmark. |
| Outcome: | The proposed method matches or improves the original fine-tuned model’s performance for each task while effectively decreasing parameter requirements by two orders of magnitude. |