HOFT: Householder Orthogonal Fine-tuning

• URL: http://arxiv.org/abs/2505.16531v1
• Date: Thu, 22 May 2025 11:20:35 GMT
• Title: HOFT: Householder Orthogonal Fine-tuning
• Authors: Alejandro Moreno Arcas, Albert Sanchis, Jorge Civera, Alfons Juan,
• Abstract summary: Householder Orthogonal Fine-tuning (HOFT) and Scaled Householder Orthogonal Fine-tuning (SHOFT) are evaluated.<n>Compared with state-of-the-art adaptation methods, HOFT and SHOFT show comparable or better results.
• Score: 45.8130844084218
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Adaptation of foundation models using low-rank methods is a widespread approach. Another way to adapt these models is to employ orthogonal fine-tuning methods, which are less time and memory efficient despite their good generalization properties. In this work, we propose Householder Orthogonal Fine-tuning (HOFT), a novel orthogonal fine-tuning method that aims to alleviate time and space complexity. Moreover, some theoretical properties of the orthogonal fine-tuning paradigm are explored. From this exploration, Scaled Householder Orthogonal Fine-tuning (SHOFT) is proposed. Both HOFT and SHOFT are evaluated in downstream tasks, namely commonsense reasoning, machine translation, subject-driven generation and mathematical reasoning. Compared with state-of-the-art adaptation methods, HOFT and SHOFT show comparable or better results.

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