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PiCa: Parameter-Efficient Fine-Tuning with Column Space Projection

Created by
  • Haebom

Author

Junseo Hwang, Wonguk Cho, Taesup Kim

Outline

This paper presents research on fine-tuning large-scale base models, which is essential for building expert models tailored to specific tasks and domains. Specifically, we propose PiCa (Parameter-efficient Fine-tuning with Column Space Projection), a novel method for parameter-efficient fine-tuning. PiCa projects gradients of pre-trained weights onto the main column space, providing effective inductive bias for adaptation, and further improves parameter efficiency through a novel weight sharing strategy. PiCa outperforms existing state-of-the-art methods on a variety of NLP and vision tasks.

Takeaways, Limitations

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Takeaways:
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A novel parameter-efficient fine-tuning methodology with theoretical basis is presented (PiCa).
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Enables efficient adaptation by leveraging the geometric properties of pre-trained weights.
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Demonstrated superior performance to existing SOTA methods in various tasks in NLP and vision fields.
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Improve parameter efficiency to reduce training costs, storage space, caching, and deployment overhead.
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Limitations:
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Limitations is not directly mentioned in the paper itself (this may be revealed through further research).
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The possibility that PiCa's performance may be limited to specific tasks or datasets.
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The complexity and implementation difficulties of PiCa.
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Lack of comparative studies with other PEFT techniques.
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