Problem Description

The introduction of ChatGPT has led to a significant increase in the utilization of Large Language Models (LLMs) for addressing downstream tasks. There’s an increasing focus on cost-efficient training and deployment within this context. Low-cost training and deployment of LLMs represent the future development trend. This paper reviews the evolution of large language model training techniques and inference deployment technologies aligned with this emerging trend. The discussion on training includes various aspects, including data preprocessing, training architecture, pre-training tasks, parallel training, and relevant content related to model fine-tuning. On the inference side, the paper covers topics such as model compression, parallel computation, memory scheduling, and structural optimization. It also explores LLMs’ utilization and provides insights into their future development.

Summary of the Paper

In today’s paper, I would like to introduce you to the research titled “Understanding LLMs: A Comprehensive Overview from Training to Inference,” published on Jan 6, 2024.

You can access my summary of the paper, which is available in both English and Vietnamese, here on Overleaf.

Thank You and Invitation for Feedback

Thank you for taking the time to read today’s summary! Your feedback is invaluable to me, and I encourage you to share your thoughts and suggestions. If you have any ideas for improvement or topics you’d like to see covered in future summaries, please don’t hesitate to reach out.

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Welcome to my daily paper series, and I hope you find it insightful and engaging!