Summary of Recursive Speculative Decoding: Accelerating Llm Inference Via Sampling Without Replacement, by Wonseok Jeon et al.
Recursive Speculative Decoding: Accelerating LLM Inference via Sampling Without Replacement
by Wonseok Jeon, Mukul Gagrani, Raghavv Goel, Junyoung Park, Mingu Lee, Christopher Lott
First submitted to arxiv on: 21 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 The paper proposes a novel method called Recursive Speculative Decoding (RSD) to accelerate inference in large language models. RSD is a tree-based approach that leverages the diversifiability of the draft-token tree to generate sequences. Unlike existing methods, RSD samples draft tokens without replacement and maximizes the diversity of the tree. The authors demonstrate the effectiveness of RSD using Llama 2 and OPT models, showing consistent performance gains over baseline methods for both fixed sequence lengths and computational budgets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to help computers understand language more quickly. It’s called Recursive Speculative Decoding (RSD). RSD is a special kind of process that helps large computer models learn from small computer models. This makes it faster and better at understanding language. The authors tested RSD with two popular computer models, Llama 2 and OPT, and found that it works well and is more efficient than previous methods. |
Keywords
* Artificial intelligence * Inference * Llama * Token
