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![[논문 리뷰] MoDULA: Mixture of Domain-Specific and Universal LoRA for Multi ...](https://moonlight-paper-snapshot.s3.ap-northeast-2.amazonaws.com/arxiv/modula-mixture-of-domain-specific-and-universal-lora-for-multi-task-learning-1.png)



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![[2402.01684] A Framework to Implement 1+N Multi-task Fine-tuning ...](https://ar5iv.labs.arxiv.org/html/2402.01684/assets/x1.png)




![[논문 리뷰] MeTA-LoRA: Data-Efficient Multi-Task Fine-Tuning for Large ...](https://moonlight-paper-snapshot.s3.ap-northeast-2.amazonaws.com/arxiv/meta-lora-data-efficient-multi-task-fine-tuning-for-large-language-models-2.png)

![[论文评述] MTL-LoRA: Low-Rank Adaptation for Multi-Task Learning](https://moonlight-paper-snapshot.s3.ap-northeast-2.amazonaws.com/arxiv/mtl-lora-low-rank-adaptation-for-multi-task-learning-0.png)

























![[论文评述] R-LoRA: Random Initialization of Multi-Head LoRA for Multi-Task ...](https://moonlight-paper-snapshot.s3.ap-northeast-2.amazonaws.com/arxiv/r-lora-random-initialization-of-multi-head-lora-for-multi-task-learning-1.png)



![[논문 리뷰] Each Rank Could be an Expert: Single-Ranked Mixture of Experts ...](https://moonlight-paper-snapshot.s3.ap-northeast-2.amazonaws.com/arxiv/each-rank-could-be-an-expert-single-ranked-mixture-of-experts-lora-for-multi-task-learning-1.png)
















![[2403.03432] Mixture-of-LoRAs: An Efficient Multitask Tuning for Large ...](https://ar5iv.labs.arxiv.org/html/2403.03432/assets/parameter_fusion_2.png)








![[論文レビュー] DLP-LoRA: Efficient Task-Specific LoRA Fusion with a Dynamic ...](https://moonlight-paper-snapshot.s3.ap-northeast-2.amazonaws.com/arxiv/dlp-lora-efficient-task-specific-lora-fusion-with-a-dynamic-lightweight-plugin-for-large-language-models-2.png)



![[2402.01684] A Framework to Implement 1+N Multi-task Fine-tuning ...](https://ar5iv.labs.arxiv.org/html/2402.01684/assets/x3.png)










![[2402.01684] A Framework to Implement 1+N Multi-task Fine-tuning ...](https://ar5iv.labs.arxiv.org/html/2402.01684/assets/x2.png)




















