Summary of Mend: Meta Demonstration Distillation For Efficient and Effective In-context Learning, by Yichuan Li et al.
MEND: Meta dEmonstratioN Distillation for Efficient and Effective In-Context Learning
by Yichuan Li, Xiyao Ma, Sixing Lu, Kyumin Lee, Xiaohu Liu, Chenlei Guo
First submitted to arxiv on: 11 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 This paper presents Meta dEmonstratioN Distillation (MEND), a novel approach to enhance the efficiency and effectiveness of in-context learning (ICL) in Large Language Models (LLMs). MEND distills lengthy demonstrations into compact vectors without requiring task-specific retraining, achieving both efficiency and effectiveness simultaneously. The authors exploit knowledge distillation to align MEND with LLMs through a two-stage training process. Comprehensive evaluations across seven diverse ICL tasks demonstrate MEND’s prowess, matching or outperforming Vanilla ICL and state-of-the-art distillation models while reducing computational demands. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research makes it possible for large language models to learn faster and more efficiently by breaking down long examples into shorter ones. The new method, called MEND, allows these models to do this without needing to be retrained for each specific task. This means that the models can work with much less information and still produce accurate results. The authors tested MEND on many different tasks and found it worked just as well or even better than other methods while using fewer resources. |
Keywords
» Artificial intelligence » Distillation » Knowledge distillation
