Papers with SeLoRA

1 papers
Revisiting LoRA through the Lens of Parameter Redundancy: Spectral Encoding Helps (2025.findings-acl)

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Challenge: Low-Rank Adaptation (LoRA) has emerged as a prominent technique for fine-tuning large foundation models.
Approach: They propose a low-rank Adaptation technique that harnesses the expressiveness of spectral bases to re-parameterize LoRA from a sparse spectral subspace.
Outcome: The proposed technique achieves greater efficiency with fewer parameters than baselines on various downstream tasks, including commonsense reasoning, math reasoning, and code generation.

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