Summary of Elad: Explanation-guided Large Language Models Active Distillation, by Yifei Zhang et al.
ELAD: Explanation-Guided Large Language Models Active Distillation
by Yifei Zhang, Bo Pan, Chen Ling, Yuntong Hu, Liang Zhao
First submitted to arxiv on: 20 Feb 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 Explanation-Guided LLMs Active Distillation (ELAD) framework tackles the limitations of Large Language Models (LLMs), such as memory inefficiency, computational demands, and high API inference costs. By introducing an active learning strategy, ELAD optimizes the balance between annotation costs and model performance. The framework also includes an explanation-guided sample selection method that identifies challenging samples and a customized LLM-annotated explanation revision technique to correct flaws in the student model’s reasoning. Our experiments across various datasets demonstrate significant efficiency gains in LLM knowledge distillation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are powerful tools, but they can be expensive and slow. Researchers tried to fix this by transferring the LLM’s capabilities to smaller models, but it didn’t work well. They didn’t know if they were getting the right information or not. A new method called ELAD helps solve this problem. It uses an active learning strategy to find the most important information to learn and an explanation-guided sample selection method to identify tricky questions. The result is a more efficient way to get the benefits of LLMs. |
Keywords
» Artificial intelligence » Active learning » Distillation » Inference » Knowledge distillation » Student model
