Fine-tuning Happens in Tiny Subspaces: Exploring Intrinsic Task-specific Subspaces of Pre-trained Language Models (2023.acl-long)
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| Challenge: | Pre-trained language models are overly parameterized and have significant redundancy . recent studies show that PLMs are highly over-parameterized and robust to pruning . |
| Approach: | They propose to re-parameter and fine-tune pre-trained language models from a new perspective: Discovery of intrinsic task-specific subspace. |
| Outcome: | The proposed model can be fine-tuned in the subspace with a small number of free parameters. |
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| Challenge: | Multilingual neural machine translation models support fine-tuning hundreds of languages simultaneously. |
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| Challenge: | Existing methods for parameter-efficient fine-tuning have been proposed to reduce time and resource costs. |
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| Challenge: | Existing methods for fine-tuning pre-trained language models are ineffective, despite their potential, pre-training models suffer from important weaknesses. |
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| Challenge: | Pre-trained large language models retain task-specific knowledge, but where and to what extent they retain it remains unexplored. |
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Xin Zhou, Ruotian Ma, Yicheng Zou, Xuanting Chen, Tao Gui, Qi Zhang, Xuanjing Huang, Rui Xie, Wei Wu
| Challenge: | Large pre-trained language models (PLMs) have demonstrated superior performance in industrial applications. |
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Small Pre-trained Language Models Can be Fine-tuned as Large Models via Over-Parameterization (2023.acl-long)
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| Challenge: | Large pre-trained language models (PLMs) have shown remarkable performance in various natural language processing tasks, outperforming small PLMs by a large margin. |
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A Study of Parameter Efficient Fine-tuning by Learning to Efficiently Fine-Tune (2024.findings-emnlp)
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| Challenge: | Existing methods for parameter-efficient fine-tuning (PEFT) are limited due to the need for increased computational resources. |
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