Summary of Towards Optimizing the Costs Of Llm Usage, by Shivanshu Shekhar et al.
Towards Optimizing the Costs of LLM Usage
by Shivanshu Shekhar, Tanishq Dubey, Koyel Mukherjee, Apoorv Saxena, Atharv Tyagi, Nishanth Kotla
First submitted to arxiv on: 29 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 This research paper explores the limitations and challenges of using Large Language Models (LLMs) in document processing tasks such as question answering and summarization. While LLMs have revolutionized natural language processing, each model has its unique strengths and weaknesses, making it crucial to understand their capabilities, costs, tokenization, and latency for specific use cases. The paper highlights the significant expenses incurred by enterprises that operate or utilize LLMs, emphasizing the need for a more informed approach to selecting and utilizing these powerful AI models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are super smart computers that can help us with tasks like answering questions and summarizing texts. But each of these models is good at different things and costs money to use. Some models are fast but not very accurate, while others are slow but very good at their job. Companies already spend a lot of money using these models for various projects. |
Keywords
* Artificial intelligence * Natural language processing * Question answering * Summarization * Tokenization
