R-LoRA: Random Initialization of Multi-Head LoRA for Multi-Task Learning

• URL: http://arxiv.org/abs/2502.15455v1
• Date: Fri, 21 Feb 2025 13:30:21 GMT
• Title: R-LoRA: Random Initialization of Multi-Head LoRA for Multi-Task Learning
• Authors: Jinda Liu, Yi Chang, Yuan Wu,
• Abstract summary: Low-rank Adaptation (LoRA) is one of the most popular parameter-efficient fine-tuning methods.<n>We propose R-LoRA, which incorporates Multi-Head Randomization.<n>Experiments demonstrate that R-LoRA is better at capturing task-specific knowledge.
• Score: 12.431575579432458
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fine-tuning large language models (LLMs) is prohibitively expensive in terms of computational and memory costs. Low-rank Adaptation (LoRA), as one of the most popular parameter-efficient fine-tuning (PEFT) methods, offers a cost-effective alternative by approximating the model changes $\Delta W \in \mathbb{R}^{m \times n}$ through the product of down-projection matrix $A \in \mathbb{R}^{m \times r}$ and head matrix $B \in \mathbb{R}^{r \times n}$, where $r \ll \min(m, n)$. In real-world scenarios, LLMs are fine-tuned on data from multiple domains to perform tasks across various fields, embodying multi-task learning (MTL). LoRA often underperforms in such complex scenarios. To enhance LoRA's capability in multi-task learning, we propose R-LoRA, which incorporates Multi-Head Randomization. Multi-Head Randomization diversifies the head matrices through Multi-Head Random Initialization and Multi-Head Dropout, enabling more efficient learning of task-specific features while maintaining shared knowledge representation. Extensive experiments demonstrate that R-LoRA is better at capturing task-specific knowledge, thereby improving performance in multi-task scenarios. The code is available at https://github.com/jinda-liu/R-LoRA.

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