HiRA: Parameter-Efficient Hadamard High-Rank Adaptation for Large Language Models

Qiushi Huang, Tom Ko, Zhan Zhuang, Lilian Tang, Yu Zhang

Keywords: Parametric-efficient fine-tuning, Large Language Model

Abstract: We propose Hadamard High-Rank Adaptation (HiRA), a parameter-efficient fine-tuning (PEFT) method that enhances the adaptability of Large Language Models (LLMs). While Low-rank Adaptation (LoRA) is widely used to reduce resource demands, its low-rank updates may limit its expressiveness for new tasks. HiRA addresses this by using a Hadamard product to retain high-rank update parameters, improving the model capacity. Empirically, HiRA outperforms LoRA and its variants on several tasks, with extensive ablation studies validating its effectiveness. Our code is available at https://github.com/hqsiswiliam/hira.

Primary Area: other topics in machine learning (i.e., none of the above)

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Submission Number: 5572