GeLoRA: Geometric Adaptive Ranks For Efficient LoRA Fine-tuning (2025.findings-emnlp)
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| Challenge: | Existing adaptive LoRA methods lack a theoretical foundation to guide this trade-off optimally. |
| Approach: | They propose a principled approach that estimates the intrinsic dimensionality of hidden data representations to adaptively select LoRA ranks. |
| Outcome: | Experiments show that GeLoRA outperforms adaptive LoRA methods by up to +1.0% . |
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| Challenge: | Low-rank adaptation (LoRA) has demonstrated commendable performance as a popular method . however, it is implemented with a fixed intrinsic rank that might not be ideal for downstream tasks. |
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