Summary of Mtl-lora: Low-rank Adaptation For Multi-task Learning, by Yaming Yang et al.
MTL-LoRA: Low-Rank Adaptation for Multi-Task Learning
by Yaming Yang, Dilxat Muhtar, Yelong Shen, Yuefeng Zhan, Jianfeng Liu, Yujing Wang, Hao Sun, Denvy Deng, Feng Sun, Qi Zhang, Weizhu Chen, Yunhai Tong
First submitted to arxiv on: 12 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Parameter-efficient fine-tuning (PEFT) has been widely used for domain adaptation, particularly with the LoRA method due to its simplicity and effectiveness. However, in multi-task learning (MTL) scenarios, LoRA tends to obscure task distinctions by projecting high-dimensional features from different tasks into the same low-dimensional space. This leads to task interference and suboptimal performance. To address this challenge, MTL-LoRA is proposed, augmenting LoRA with additional task-adaptive parameters that differentiate task-specific information and capture shared knowledge across tasks in low-dimensional spaces. This approach enables large language models (LLMs) pre-trained on general corpus to adapt to different target task domains with a limited number of trainable parameters. Evaluations on public benchmarks for natural language understanding, commonsense reasoning, image-text understanding, and real-world industrial text Ads relevance datasets demonstrate that MTL-LoRA outperforms LoRA and its variants, using comparable or even fewer learnable parameters in multitask learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a super smart computer program that can understand lots of things. You want it to get even better at understanding new types of text, like ads on social media. One way to do this is by “fine-tuning” the program’s language skills for each type of text. LoRA is one popular method that works pretty well. But sometimes it gets confused when trying to learn many different types of text at once. This makes it not perform as well as it could. To fix this, researchers came up with a new idea called MTL-LoRA. It’s like a special filter that helps the program understand each type of text better while also learning from all the other texts. This way, it can get really good at understanding lots of different types of text without getting confused. The results show that MTL-LoRA works even better than LoRA and uses fewer “brain cells” to do it! |
Keywords
» Artificial intelligence » Domain adaptation » Fine tuning » Language understanding » Lora » Multi task » Parameter efficient
