Summary of Suffixdecoding: a Model-free Approach to Speeding Up Large Language Model Inference, by Gabriele Oliaro et al.
SuffixDecoding: A Model-Free Approach to Speeding Up Large Language Model Inference
by Gabriele Oliaro, Zhihao Jia, Daniel Campos, Aurick Qiao
First submitted to arxiv on: 7 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); 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 This novel approach to accelerating large language model (LLM) inference is called SuffixDecoding. It’s a model-free method that uses suffix trees built from previously generated text to predict candidate token sequences efficiently. Unlike other methods, SuffixDecoding doesn’t rely on draft models or specialized decoding heads, which makes it more flexible and efficient. The approach builds and updates suffix trees dynamically to capture patterns in the generated text, using them to construct speculation trees through a principled scoring mechanism based on empirical token frequencies. This requires only CPU memory, which is plentiful and underutilized on typical LLM serving nodes. SuffixDecoding achieves competitive speedups compared to model-based approaches across diverse workloads including open-domain chat, code generation, and text-to-SQL tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SuffixDecoding is a new way to make language models run faster. It uses special trees called suffix trees to guess what might come next in the text. This helps it predict words and phrases more quickly. Unlike other methods that need extra help from separate models or special tools, SuffixDecoding just uses what’s already been written. It can even get better as it looks at more examples! The results show that SuffixDecoding is faster than some other approaches for tasks like chatbots, writing code, and translating text into SQL language. |
Keywords
» Artificial intelligence » Inference » Large language model » Token
