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The scaling up of the decoder-only transformer model opens up a world to get powerful language models that can generate novel content and response for queries. However, to make a practical AI assistant that interacts with billions of users, we need the model to not only be a strong language model, but also to understand diverse task requirements and align with human values. Larger model architectures, high user volumes, and long query contexts are all bringing challenges to the performance and resource consumptions of the system hosting these models. In this chapter, we take the leap from language model to large language model (LLM) to understand how the trending AI assistants are produced, deployed, and optimized.

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Large Language Models

  • 章节

  • 发表日期：

    2026年

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  • Yiran Chen,
  • Hai Li,
  • Huanrui Yang

摘要

The scaling up of the decoder-only transformer model opens up a world to get powerful language models that can generate novel content and response for queries. However, to make a practical AI assistant that interacts with billions of users, we need the model to not only be a strong language model, but also to understand diverse task requirements and align with human values. Larger model architectures, high user volumes, and long query contexts are all bringing challenges to the performance and resource consumptions of the system hosting these models. In this chapter, we take the leap from language model to large language model (LLM) to understand how the trending AI assistants are produced, deployed, and optimized.