Summary of A Survey Of Sustainability in Large Language Models: Applications, Economics, and Challenges, by Aditi Singh et al.
A Survey of Sustainability in Large Language Models: Applications, Economics, and Challenges
by Aditi Singh, Nirmal Prakashbhai Patel, Abul Ehtesham, Saket Kumar, Tala Talaei Khoei
First submitted to arxiv on: 6 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computational Engineering, Finance, and Science (cs.CE)
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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 LLMs have revolutionized various fields by providing advanced capabilities in natural language understanding, generation, and reasoning. Despite their groundbreaking applications across industries such as research, healthcare, and creative media, their rapid adoption raises critical concerns regarding sustainability. This survey paper comprehensively examines the environmental, economic, and computational challenges associated with LLMs, focusing on energy consumption, carbon emissions, and resource utilization in data centers. By synthesizing insights from existing literature, this work explores strategies such as resource-efficient training, sustainable deployment practices, and lifecycle assessments to mitigate the environmental impacts of LLMs. Key areas of emphasis include energy optimization, renewable energy integration, and balancing performance with sustainability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LLMs have made a big impact in many fields by helping computers understand, generate, and reason about human language. But their fast adoption has raised important questions about how to make sure they’re sustainable. This paper looks at the environmental, economic, and computer-related challenges that come with using LLMs, focusing on how much energy they use, how much carbon they produce, and what resources are needed to run them. The paper also explores ways to make LLMs more environmentally friendly, such as using less energy, integrating renewable energy, and balancing performance with sustainability. |
Keywords
» Artificial intelligence » Language understanding » Optimization
