Summary of Llmr: Knowledge Distillation with a Large Language Model-induced Reward, by Dongheng Li et al.
LLMR: Knowledge Distillation with a Large Language Model-Induced Reward
by Dongheng Li, Yongchang Hao, Lili Mou
First submitted to arxiv on: 19 Sep 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 LLMR method, a novel knowledge distillation (KD) technique based on a reward function induced from large language models, significantly improves performance in dialogue generation and summarization tasks. By leveraging the power of large language models while reducing computational costs, LLMR shows consistent outperformance over traditional KD methods across multiple datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers developed a new way to make big language models more accessible by using rewards from those same models. They tested this method on conversations and summaries and found that it worked better than usual methods in many cases. This is important because large language models are powerful but can be too demanding for some computers or devices. |
Keywords
» Artificial intelligence » Knowledge distillation » Summarization
