Summary of Deploying Multi-task Online Server with Large Language Model, by Yincen Qu et al.
Deploying Multi-task Online Server with Large Language Model
by Yincen Qu, Chao Ma, Xiangying Dai, Hui Zhou, Yiting Wu, Hengyue Liu
First submitted to arxiv on: 6 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 The paper proposes a three-stage multi-task learning framework for large language models to tackle the challenges of developing and scaling models in real-world applications. The framework involves task filtering, fine-tuning on high-resource tasks, and finally fine-tuning on all tasks. The approach is evaluated through comprehensive experiments in single-task and multi-task settings, demonstrating that it can achieve performance comparable to the single-task method while reducing up to 90.9% of its overhead. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers have developed a new way for large language models to learn multiple tasks at once, without sacrificing performance. This approach helps reduce the cost and complexity of building these models, which is important for real-world applications. The model is tested on different datasets and shows that it can perform just as well as single-task models while using fewer resources. |
Keywords
» Artificial intelligence » Fine tuning » Multi task
