Summary of Speculative Decoding with Ctc-based Draft Model For Llm Inference Acceleration, by Zhuofan Wen et al.
Speculative Decoding with CTC-based Draft Model for LLM Inference Acceleration
by Zhuofan Wen, Shangtong Gui, Yang Feng
First submitted to arxiv on: 25 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 proposes an approach to accelerate the inference of large language models (LLMs) by improving the performance of draft models used in speculative decoding. The current draft models generate tokens non-autoregressively, resulting in high decoding speeds but low acceptance rates. To address this, the authors introduce a Connectionist Temporal Classification (CTC)-based draft model that strengthens correlations between draft tokens during the draft phase. This leads to higher-quality draft candidate sequences and improved inference speed. The proposed method outperforms strong baselines, achieving a higher acceptance rate and faster inference speed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making language models work faster. Right now, these models use a “draft” process that helps them make good decisions quickly. But this process can be slow and not very accurate. The researchers propose a new way to do the draft process using something called CTC. This helps the model make better guesses and get its answers more quickly. They test their idea and find that it works better than other ways of doing things, making language models faster and more accurate. |
Keywords
» Artificial intelligence » Classification » Inference
