Summary of Lossless Acceleration Of Large Language Model Via Adaptive N-gram Parallel Decoding, by Jie Ou et al.
Lossless Acceleration of Large Language Model via Adaptive N-gram Parallel Decoding
by Jie Ou, Yueming Chen, Wenhong Tian
First submitted to arxiv on: 10 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 paper introduces Adaptive N-gram Parallel Decoding (ANPD), a novel approach that accelerates inference in Large Language Models (LLMs) while preserving their original output integrity. ANPD employs a two-stage process: rapid drafting using an N-gram module and verification by the LLM. This method eliminates the need for retraining or extra GPU memory, making it efficient and plug-and-play. The paper showcases speed improvements up to 3.67x in models like LLaMA and its fine-tuned variants. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are super smart, but they take a long time to work because of how they process information. This new approach, called ANPD, makes them faster without losing their intelligence. It’s like having a super-fast writing partner that checks facts with the original expert. The new method is simple, doesn’t require extra training or special computers, and makes LLMs up to 3.67 times quicker. |
Keywords
* Artificial intelligence * Inference * Llama * N gram
