Summary of Towards Coarse-to-fine Evaluation Of Inference Efficiency For Large Language Models, by Yushuo Chen et al.
Towards Coarse-to-Fine Evaluation of Inference Efficiency for Large Language Models
by Yushuo Chen, Tianyi Tang, Erge Xiang, Linjiang Li, Wayne Xin Zhao, Jing Wang, Yunpeng Chai, Ji-Rong Wen
First submitted to arxiv on: 17 Apr 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 research paper proposes a detailed analysis of large language model (LLM) inference efficiency, focusing on various optimization algorithms and code libraries developed to improve performance. The authors conduct a coarse-to-fine evaluation of these methods across four usage scenarios in two practical applications. Their experiments provide comprehensive results that can help researchers compare the effectiveness of different approaches and optimize LLM inference strategies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) are becoming essential assistants for people in their daily work, as well as supporting advanced applications. To make them more efficient, many optimization algorithms and code libraries have been developed to improve performance. However, users still struggle to understand which methods work best and why. This research studies the effectiveness of different code libraries by analyzing their performance in various situations. The results are very helpful for researchers trying to develop better language models. |
Keywords
» Artificial intelligence » Inference » Large language model » Optimization
