Summary of Optimized Multi-token Joint Decoding with Auxiliary Model For Llm Inference, by Zongyue Qin et al.
Optimized Multi-Token Joint Decoding with Auxiliary Model for LLM Inference
by Zongyue Qin, Ziniu Hu, Zifan He, Neha Prakriya, Jason Cong, Yizhou Sun
First submitted to arxiv on: 12 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 The proposed paper introduces a novel framework called Multi-Token Assisted Decoding (MTAD) that simultaneously enhances inference speed and improves output effectiveness for Large Language Models (LLMs). MTAD leverages a smaller auxiliary model to approximate the joint distribution of a larger model, incorporating a verification mechanism to ensure accuracy and improve decoding efficiency. The authors demonstrate that MTAD closely approximates exact multi-token joint decoding with bounded error. Experimental results using Llama-2 and OPT models ranging from 13B to 70B parameters across various tasks show that MTAD reduces perplexity by 21.2% and improves downstream performance compared to standard single-token sampling, while achieving a 1.42x speed-up and consuming 1.54x less energy than conventional speculative decoding methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to make Large Language Models work faster and better. It uses a smaller model to help a bigger model understand how to generate multiple words at once, instead of one word at a time. This makes the process more efficient and gets better results. The authors tested their method on different models and tasks and found that it reduced errors by 21.2% and got better results in the end, all while being faster and using less energy. |
Keywords
» Artificial intelligence » Inference » Llama » Perplexity » Token
