Summary of Adaptive Draft-verification For Efficient Large Language Model Decoding, by Xukun Liu et al.
Adaptive Draft-Verification for Efficient Large Language Model Decoding
by Xukun Liu, Bowen Lei, Ruqi Zhang, Dongkuan Xu
First submitted to arxiv on: 27 Jun 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 The paper proposes an efficient method for large language model (LLM) decoding called Adaptive Draft-Verification and Evaluation (ADED). The traditional autoregressive decoding method is computationally inefficient and poses challenges for deploying LLMs in latency-sensitive scenarios. ADED accelerates the decoding process without requiring fine-tuning, using a tri-gram matrix-based LLM representation to dynamically approximate the output distribution of the LLM. The approach involves an adaptive draft-verification process that evolves over time to improve efficiency. Additionally, a draft construction mechanism balances exploration and exploitation, ensuring diverse and close-to-optimal drafts are generated. Through experiments on various benchmark datasets and LLM architectures, ADED significantly accelerates decoding while maintaining high accuracy, making it suitable for practical applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to predict the next word in a sentence, like a game of word guessing. The computer has to make many guesses one at a time, which can be slow and tricky. To solve this problem, researchers developed a new way called ADED (Adaptive Draft-Verification and Evaluation). This method helps computers guess words more quickly and accurately by using special techniques and rules. They tested their idea on different datasets and language models to see how well it worked. The results showed that ADED can make word guessing faster and better, making it useful for many practical applications. |
Keywords
» Artificial intelligence » Autoregressive » Fine tuning » Large language model
