Summary of Snapkv: Llm Knows What You Are Looking For Before Generation, by Yuhong Li et al.
SnapKV: LLM Knows What You are Looking for Before Generation
by Yuhong Li, Yingbing Huang, Bowen Yang, Bharat Venkitesh, Acyr Locatelli, Hanchen Ye, Tianle Cai, Patrick Lewis, Deming Chen
First submitted to arxiv on: 22 Apr 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 SnapKV method aims to tackle the issue of Large Language Models’ (LLMs) Key-Value (KV) caches growing excessively with increasing input length. By introducing a fine-tuning-free approach, SnapKV efficiently reduces KV cache size while maintaining comparable performance in real-world applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SnapKV is an innovative solution that solves the problem of Large Language Models’ (LLMs) Key-Value (KV) caches getting bigger when processing longer texts. It helps memory and time efficiency without needing to fine-tune the model. |
Keywords
» Artificial intelligence » Fine tuning
