Statistical Inference for Rank Allocation in Low-Rank Adaptation
Low-rank adaptation (LoRA) has become a widely used parameter-efficient fine-tuning method for large language models. Since different modules and layers may contribute unequally to downstream adaptation, allocating rank resources under a fixed parameter budget is an important problem for balancing efficiency, expressiveness, and generalization. Existing adaptive rank methods address this problem mainly through carefully designed importance scores constructed from gradient-derived sensitivity and uncertainty measures, without an explicit statistical interpretation. In this paper, we formulate L
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- FuzzySimilar title/name (fuzzy) · 84%xorbitsai/inference →
“Fuzzy title match (0.92): “Statistical Inference for Rank Allocation in Low-Rank Adapta” ≈ “xorbitsai/inference””
- LinkedLinked via arxiv author · 85%Yihang Gao →
“Statistical Inference for Rank Allocation in Low-Rank Adaptation”
- LinkedLinked via arxiv author · 85%Vincent Y. F. Tan →
“Statistical Inference for Rank Allocation in Low-Rank Adaptation”