Summary of Falcon: Faster and Parallel Inference Of Large Language Models Through Enhanced Semi-autoregressive Drafting and Custom-designed Decoding Tree, by Xiangxiang Gao and Weisheng Xie and Yiwei Xiang and Feng Ji
Falcon: Faster and Parallel Inference of Large Language Models through Enhanced Semi-Autoregressive Drafting and Custom-Designed Decoding Tree
by Xiangxiang Gao, Weisheng Xie, Yiwei Xiang, Feng Ji
First submitted to arxiv on: 17 Dec 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 This paper introduces Falcon, an innovative semi-autoregressive speculative decoding framework designed to balance minimal drafting latency with high speculation accuracy in Large Language Models (LLMs). The framework incorporates the Coupled Sequential Glancing Distillation technique, which enhances inter-token dependencies and increases speculation accuracy. Additionally, it features a Custom-Designed Decoding Tree that allows for multiple tokens generation in a single forward pass, boosting drafted tokens and acceptance rate. Comprehensive evaluations on benchmark datasets such as MT-Bench, HumanEval, and GSM8K demonstrate Falcon’s superior acceleration capabilities, achieving a lossless speedup ratio ranging from 2.91x to 3.51x with the Vicuna and LLaMA2-Chat model series. These results outstrip existing methods for LLMs, including Eagle, Medusa, Lookahead, SPS, and PLD. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Falcon is a new way to improve Large Language Models (LLMs). It helps these models make good guesses quickly. The old way was slow and not very accurate. Falcon makes it faster and more accurate by using something called Coupled Sequential Glancing Distillation. This helps the model understand how words are related better. Another key part is a special tree that lets the model generate many tokens at once, which makes it even faster. |
Keywords
» Artificial intelligence » Autoregressive » Boosting » Distillation » Token
