@inproceedings{zhang-etal-2025-orthogonal,
    title = "An Orthogonal High-Rank Adaptation for Large Language Models",
    author = "Zhang, Xin  and
      Chen, Guang-Ze  and
      Li, Shuzhen  and
      Liu, Zhulin  and
      Chen, C.L.Philip  and
      Zhang, Tong",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.951/",
    pages = "18826--18844",
    ISBN = "979-8-89176-332-6",
    abstract = "Low-rank adaptation (LoRA) efficiently adapts LLMs to downstream tasks by decomposing LLMs' weight update into trainable low-rank matrices for fine-tuning. However, the random low-rank matrices may introduce massive task-irrelevant information, while their recomposed form suffer from limited representation spaces under low-rank operations. Such dense and choked adaptation in LoRA impairs the adaptation performance of LLMs on downstream tasks. To address these challenges, this paper proposes OHoRA, an orthogonal high-rank adaptation for parameter-efficient fine-tuning on LLMs. According to the information bottleneck theory, OHoRA decomposes LLMs' pre-trained weight matrices into orthogonal basis vectors via QR decomposition and splits them into two low-redundancy high-rank components to suppress task-irrelevant information. It then performs dynamic rank-elevated recomposition through Kronecker product to generate expansive task-tailored representation spaces, enabling precise LLM adaptation and enhanced generalization. OHoRA effectively operationalizes the information bottleneck theory to decompose LLMs' weight matrices into low-redundancy high-rank components and recompose them in rank-elevated manner for more task-tailored representation spaces and precise LLM adaptation. Empirical evaluation shows OHoRA{'}s effectiveness by outperforming LoRA and its variants and achieving comparable performance to full fine-tuning with only 0.0371{\%} trainable parameters."
}