MoDE: Effective Multi-task Parameter Efficient Fine-Tuning with a Mixture of Dyadic Experts (2025.findings-naacl)
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| Challenge: | Recent efforts have explored mixtures of LoRA modules for multi-task settings, but this study reveals redundancy in the down-projection matrix of these architectures. |
| Approach: | They propose a method to share down-projection matrix across tasks and employ atomic rank-one adapters coupled with routers that allow more sophisticated task-level specialization. |
| Outcome: | The proposed method outperforms state-of-the-art models on a SNI benchmark and provides a practical solution for deploying lightweight models. |
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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. |
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