Hebbian-Guided Bi-Directional Rank Adaptation for Parameter-Efficient Fine-Tuning (2026.findings-acl)
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| Challenge: | Low-Rank Adaptation (LoRA) is a widely used method to fine-tune large language models . but its fixed-rank design cannot capture the varying importance across different layers . |
| Approach: | They propose a framework that bi-directionally reallocates low-rank capacity using Hebbian-inspired importance estimation. |
| Outcome: | Experiments show that HeBiRA improves performance over baselines. |
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