Papers by Xiushuang Yi
MoKA:Parameter Efficiency Fine-Tuning via Mixture of Kronecker Product Adaption (2025.coling-main)
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| Challenge: | Low-Rank Adaptation (LoRA) is one of the most popular PEFT methods . low-rank update mechanism of LoRA somewhat limits its ability to approximate full-parameter fine-tuning during training process. |
| Approach: | They propose a parameter-efficient fine-tuning framework that combines Kronecker product with the Mixture-of-Experts method to achieve parameter efficiency and better model performance. |
| Outcome: | The proposed framework outperforms existing methods on the GLUE benchmark and instruction tuning tasks for large language models. |