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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.23123 |
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| _version_ | 1866909870859485184 |
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| author | Li, Shiwei Luo, Xiandi Wang, Haozhao Tang, Xing Cui, Ziqiang Liu, Dugang Li, Yuhua He, Xiuqiang Li, Ruixuan |
| author_facet | Li, Shiwei Luo, Xiandi Wang, Haozhao Tang, Xing Cui, Ziqiang Liu, Dugang Li, Yuhua He, Xiuqiang Li, Ruixuan |
| contents | Low-rank adaptation (LoRA) is a parameter-efficient fine-tuning (PEFT) method widely used in large language models (LLMs). LoRA essentially describes the projection of an input space into a low-dimensional output space, with the dimensionality determined by the LoRA rank. In standard LoRA, all input tokens share the same weights and undergo an identical input-output projection. This limits LoRA's ability to capture token-specific information due to the inherent semantic differences among tokens. To address this limitation, we propose Token-wise Projected Low-Rank Adaptation (TopLoRA), which dynamically adjusts LoRA weights according to the input token, thereby learning token-wise input-output projections in an end-to-end manner. Formally, the weights of TopLoRA can be expressed as $BΣ_X A$, where $A$ and $B$ are low-rank matrices (as in standard LoRA), and $Σ_X$ is a diagonal matrix generated from each input token $X$. Notably, TopLoRA does not increase the rank of LoRA weights but achieves more granular adaptation by learning token-wise LoRA weights (i.e., token-wise input-output projections). Extensive experiments across multiple models and datasets demonstrate that TopLoRA consistently outperforms LoRA and its variants. The code is available at https://github.com/Leopold1423/toplora-neurips25. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_23123 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Beyond Higher Rank: Token-wise Input-Output Projections for Efficient Low-Rank Adaptation Li, Shiwei Luo, Xiandi Wang, Haozhao Tang, Xing Cui, Ziqiang Liu, Dugang Li, Yuhua He, Xiuqiang Li, Ruixuan Computation and Language Machine Learning Low-rank adaptation (LoRA) is a parameter-efficient fine-tuning (PEFT) method widely used in large language models (LLMs). LoRA essentially describes the projection of an input space into a low-dimensional output space, with the dimensionality determined by the LoRA rank. In standard LoRA, all input tokens share the same weights and undergo an identical input-output projection. This limits LoRA's ability to capture token-specific information due to the inherent semantic differences among tokens. To address this limitation, we propose Token-wise Projected Low-Rank Adaptation (TopLoRA), which dynamically adjusts LoRA weights according to the input token, thereby learning token-wise input-output projections in an end-to-end manner. Formally, the weights of TopLoRA can be expressed as $BΣ_X A$, where $A$ and $B$ are low-rank matrices (as in standard LoRA), and $Σ_X$ is a diagonal matrix generated from each input token $X$. Notably, TopLoRA does not increase the rank of LoRA weights but achieves more granular adaptation by learning token-wise LoRA weights (i.e., token-wise input-output projections). Extensive experiments across multiple models and datasets demonstrate that TopLoRA consistently outperforms LoRA and its variants. The code is available at https://github.com/Leopold1423/toplora-neurips25. |
| title | Beyond Higher Rank: Token-wise Input-Output Projections for Efficient Low-Rank Adaptation |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2510.23123 |