July 25, 2024Open Access

LoRA-Pro: Are Low-Rank Adapters Properly Optimized?

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Authors

ZWZhengbo WangInstitute of Automation JLJian LiangHarbin University of Science and Technology

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Implication

Optimization analysis demonstrates narrowed performance gaps in foundation models, suggesting that equivalent gradient alignment improves low-rank adaptation.

Key Points

LoRA-Pro effectively bridges the optimization performance gap with full fine-tuning, maintaining parameter efficiency across natural language processing tasks.
Optimization analysis derives closed-form solutions by minimizing the difference between the equivalent gradient of low-rank matrices and the full gradient.
Supports improved adaptation in foundation models, indicating that equivalent gradient alignment successfully mitigates low-rank adaptation performance loss.

Cite This Study

Wang et al. (2024) studied this question.

synapsesocial.com/papers/68e5f2d2b6db6435875874aa https://doi.org/10.48550/arxiv.2407.18242

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Implication

Optimization analysis demonstrates narrowed performance gaps in foundation models, suggesting that equivalent gradient alignment improves low-rank adaptation.

LoRA-Pro: Are Low-Rank Adapters Properly Optimized?

Discussion

Member takes

Implication

Key Points

Cite This Study

LoRA-Pro: Are Low-Rank Adapters Properly Optimized?

Discussion

Member takes

Implication

Key Points

Cite This Study

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Also Consider