Papers with random low-rank matrices
An Orthogonal High-Rank Adaptation for Large Language Models (2025.emnlp-main)
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| Challenge: | Low-rank adaptation (LoRA) efficiently adapts LLMs to downstream tasks by decomposing LLM’s weight update into trainable low-rank matrices for fine-tuning. |
| Approach: | They propose an orthogonal high-rank adaptation for parameter-efficient fine-tuning that decomposes LLMs’ pre-trained weight matrices into orthogonals via QR decomposition and splits them into two low-redundancy high-ranked components. |
| Outcome: | Empirical results show that OHoRA outperforms LoRA and its variants and generates task-tailored representation spaces with 0.0371% trainable parameters. |