This tutorial explores the advancements and challenges in the development of Large Language Models (LLMs) such as ChatGPT and Gemini. It addresses inherent limitations like temporal knowledge cutoffs, mathematical inaccuracies, and the generation of incorrect information, proposing solutions like Retrieval Augmented Generation (RAG), Program-Aided Language Models (PAL), and frameworks such as ReAct and LangChain. The integration of these techniques enhances LLM performance and reliability, especially in multi-step reasoning and complex task execution. The paper also covers fine-tuning strategies, including instruction fine-tuning, parameter-efficient methods like LoRA, and Reinforcement Learning from Human Feedback (RLHF) as well as Reinforced Self-Training (ReST). Additionally, it provides a comprehensive survey of transformer architectures and training techniques for LLMs. The source code can be accessed by contacting the author via email for a request.

翻译：本教程探讨了ChatGPT和Gemini等大型语言模型（LLMs）的发展进展与挑战。针对其固有局限性，如时效性知识截止、数学不准确性及错误信息生成等问题，提出了检索增强生成（RAG）、程序辅助语言模型（PAL）以及ReAct和LangChain等框架的解决方案。这些技术的整合显著提升了LLMs在复杂任务执行和多步推理中的性能与可靠性。本文还涵盖了微调策略，包括指令微调、参数高效方法（如LoRA）、基于人类反馈的强化学习（RLHF）以及强化自训练（ReST）。此外，对LLMs的Transformer架构与训练技术进行了全面综述。相关源代码可通过邮件联系作者索取。