Summary of Clover: Regressive Lightweight Speculative Decoding with Sequential Knowledge, by Bin Xiao et al.
Clover: Regressive Lightweight Speculative Decoding with Sequential Knowledge
by Bin Xiao, Chunan Shi, Xiaonan Nie, Fan Yang, Xiangwei Deng, Lei Su, Weipeng Chen, Bin Cui
First submitted to arxiv on: 1 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 Large language models (LLMs) face a challenge in balancing auto-regressive decoding with GPU design. The mismatch between loading parameters and computing tokens results in most GPU time spent on memory transfer rather than computation. Recent parallel decoding algorithms have shown efficiency improvements but deviate from the pre-training objective, resulting in low hit rates for candidate tokens. This paper proposes Clover, a new speculative decoding algorithm that integrates sequential knowledge into parallel decoding. Clover transmits this knowledge through Regressive Connection and employs an Attention Decoder to integrate speculated tokens. It also incorporates an Augmenting Block to align with the purpose of speculative generation. The results show that Clover outperforms the baseline by up to 91% on Baichuan-Small and 146% on Baichuan-Large, respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are very big computers that can understand and generate human-like text. But they have a problem: they don’t use their computing power efficiently. This is because they need to load lots of information into memory before they can start processing. Recently, new ways of decoding (processing) text have been developed. These methods allow the model to predict multiple possible next words at once, which speeds up processing. However, these methods are not very good at guessing what comes next. This paper proposes a new way of decoding called Clover that is better at this task. It uses information from previous guesses to help make better predictions. The results show that Clover is much faster and more accurate than existing methods. |
Keywords
» Artificial intelligence » Attention » Decoder
