Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning (2021.acl-long)
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| Challenge: | Pre-trained language models can be fine-tuned to produce state-of-the-art results for a wide range of language understanding tasks. |
| Approach: | They propose to analyze fine-tuning through the lens of intrinsic dimension . they show that pre-trained models have a low intrinsic dimension reparameterization . |
| Outcome: | The proposed model can achieve 90% of the full parameter performance levels on MRPC with low data regime. |
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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 . |
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| Challenge: | Multilingual neural machine translation models support fine-tuning hundreds of languages simultaneously. |
| Approach: | They propose to fine-tune a language in its intrinsic subspace with a tiny fraction of entire parameters. |
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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: | Fine-tuning and in-context learning are two prevalent methods in imbuing large language models with task-specific knowledge. |
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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: | Recent research has illuminated the possibility of selective parameter-efficient fine-tuning, which retains the inference speed of the original model and comes at no additional computational cost. |
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| Challenge: | linguistic knowledge encoded in pre-trained contextual embeddings is poorly understood . fine-tuning can be used to investigate the representations of pre-train models . |
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| Challenge: | Pre-trained language models provide strong foundations, but effective adaptation under data scarcity requires efficient and efficient fine-tuning techniques. |
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