Summary of Tegee: Task Definition Guided Expert Ensembling For Generalizable and Few-shot Learning, by Xingwei Qu et al.
TEGEE: Task dEfinition Guided Expert Ensembling for Generalizable and Few-shot Learning
by Xingwei Qu, Yiming Liang, Yucheng Wang, Tianyu Zheng, Tommy Yue, Xingyuan Bu, Lei Ma, Stephen W. Huang, Jiajun Zhang, Yinan Shi, Chenghua Lin, Jie Fu, Ge Zhang
First submitted to arxiv on: 7 Mar 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 proposed method, TEGEE (Task Definition Guided Expert Ensembling), is a large language model that exhibits in-context learning capabilities. It can learn new tasks directly from examples provided in demonstrations, using an implicit task selection mechanism to extract and process task definitions. The model consists of two dual 3B models: one for task definition extraction and the other for learning from demonstrations. Experimental results show that TEGEE performs similarly to the LLaMA2-13B model. This modular design enables many-shot learning, supporting an unlimited number of demonstrations and enhancing continual learning capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TEGEE is a new way for large language models to learn new tasks just by looking at examples. It works by breaking down what it learns into smaller parts: extracting the definition of the task and then using that definition to generate responses. This approach helps the model learn more quickly and accurately than before, making it useful for things like learning from many demonstrations or continuing to improve over time. |
Keywords
» Artificial intelligence » Continual learning » Large language model
