Summary of Sepllm: Accelerate Large Language Models by Compressing One Segment Into One Separator, By Guoxuan Chen et al.
SepLLM: Accelerate Large Language Models by Compressing One Segment into One Separator
by Guoxuan Chen, Han Shi, Jiawei Li, Yihang Gao, Xiaozhe Ren, Yimeng Chen, Xin Jiang, Zhenguo Li, Weiyang Liu, Chao Huang
First submitted to arxiv on: 16 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large Language Models (LLMs) have achieved impressive results across various natural language processing tasks, but their large sizes pose significant computational challenges. This paper identifies a key pattern: punctuation tokens play a disproportionate role in attention scores compared to meaningful tokens. By condensing information from these segments into the separator tokens themselves without sacrificing performance, SepLLM is introduced as a plug-and-play framework that accelerates inference by compressing redundant tokens and eliminating unnecessary ones. Efficient kernels for training acceleration are also implemented. Experimental results demonstrate SepLLM’s effectiveness across training-free, training-from-scratch, and post-training settings, with notable reductions in KV cache on the GSM8K-CoT benchmark using Llama-3-8B as the backbone while maintaining comparable performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are really good at understanding language, but they’re also very big, which makes them slow. Researchers found that some words like punctuation marks have a lot of power when it comes to how well the model works. They developed a new way called SepLLM to make these models faster by getting rid of extra information and keeping only what’s important. This helps computers process lots of language data quickly without losing performance. The results show that SepLLM can make big differences in how fast it takes for computers to understand language. |
Keywords
» Artificial intelligence » Attention » Inference » Llama » Natural language processing
