Summary of Think: Thinner Key Cache by Query-driven Pruning, By Yuhui Xu et al.
ThinK: Thinner Key Cache by Query-Driven Pruning
by Yuhui Xu, Zhanming Jie, Hanze Dong, Lei Wang, Xudong Lu, Aojun Zhou, Amrita Saha, Caiming Xiong, Doyen Sahoo
First submitted to arxiv on: 30 Jul 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 proposes ThinK, a novel query-dependent KV cache pruning method to optimize memory consumption in Large Language Models (LLMs) for long-context scenarios. Existing approaches focus on sequence length, but ThinK identifies and prunes redundant channels in the attention weights, achieving over 20% reduction in KV cache memory costs while maintaining or improving model accuracy. This is demonstrated through extensive evaluations on LLaMA and Mistral models across various long-sequence datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper helps make Large Language Models work more efficiently by reducing the amount of memory they use. It does this by identifying parts of the model that are not important and removing them. This makes it possible to process longer sequences of text without running out of memory or slowing down too much. |
Keywords
» Artificial intelligence » Attention » Llama » Pruning
