Summary of Achieving Peak Performance For Large Language Models: a Systematic Review, by Zhyar Rzgar K Rostam et al.
Achieving Peak Performance for Large Language Models: A Systematic Review
by Zhyar Rzgar K Rostam, Sándor Szénási, Gábor Kertész
First submitted to arxiv on: 7 Sep 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 A systematic literature review (SLR) presents a comprehensive overview of large language models’ (LLMs) optimization and acceleration strategies for natural language processing (NLP). The paper reviews 65 publications from 2017 to December 2023, focusing on methods to reduce computational and memory costs while maintaining state-of-the-art performance. Strategies include fine-tuning pre-trained LLMs, reducing training time or computational costs, and optimizing frameworks and libraries. Taxonomies categorize optimization and acceleration strategies into three classes: LLM training, inference, and system serving. Case studies demonstrate practical approaches to address resource limitations, showcasing the importance of balancing performance with cost and efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models have become incredibly successful in understanding human language. These powerful models require a huge amount of computer power and memory. This makes it difficult for many researchers to use or create these models. To make things better, scientists have developed ways to optimize and speed up LLMs without sacrificing their accuracy. This paper looks at all the research done on this topic since 2017, focusing on what works best. It explains how different frameworks and libraries work together, and presents a system for understanding how to improve and accelerate LLMs. The study also compares different strategies and provides real-life examples of how to make these powerful models more accessible. |
Keywords
» Artificial intelligence » Fine tuning » Inference » Natural language processing » Nlp » Optimization
