Summary of Generation Meets Verification: Accelerating Large Language Model Inference with Smart Parallel Auto-correct Decoding, by Hanling Yi et al.
Generation Meets Verification: Accelerating Large Language Model Inference with Smart Parallel Auto-Correct Decoding
by Hanling Yi, Feng Lin, Hongbin Li, Peiyang Ning, Xiaotian Yu, Rong Xiao
First submitted to arxiv on: 19 Feb 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 The proposed SPACE (Smart Parallel Auto-Correct Decoding) approach aims to accelerate the inference speed of large language models with billions of parameters. By integrating semi-autoregressive inference and speculative decoding capabilities, SPACE enables autoregressive LLMs to parallelize token generation and verification. This is achieved through a specialized semi-autoregressive supervised fine-tuning process that equips existing LLMs with the ability to simultaneously predict multiple tokens. The auto-correct decoding algorithm facilitates the simultaneous generation and verification of token sequences within a single model invocation. SPACE demonstrates inference speedup ranging from 2.7x-4.0x on HumanEval-X while maintaining output quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make big language models work faster by introducing a new way to process text. It’s like having multiple computers working together to type out a long document quickly and accurately. The approach, called SPACE, uses special training techniques and algorithms to let these language models do many tasks at the same time. This makes them much faster than before, with some tests showing they’re up to 4 times quicker. |
Keywords
* Artificial intelligence * Autoregressive * Fine tuning * Inference * Supervised * Token
