深入探究大型语言模型
本课程全面深入地介绍了大型语言模型（LLMs）的发展历程、核心技术架构、训练范式（预训练、微调和对齐）、多模态扩展、提示工程、思维链推理、智能体，以及模型安全与隐私保护等前沿话题。
课程概述
📚 内容概要
本课程全面深入地介绍了大语言模型（LLMs）的演进历程、核心技术架构、训练范式（预训练、微调与对齐）、多模态扩展、提示工程、思维链（CoT）、智能体，以及模型安全与隐私保护等前沿议题。
从预训练到通用智能体，深度剖析全栈大模型的技术演进与安全对齐。
🎯 学习目标
- 区分模型架构：识别仅编码器（Encoder-only）、仅解码器（Decoder-only）和编码器-解码器（Encoder-Decoder）模型在结构上的差异及其应用场景。
- 解释大语言模型训练流程：描述从自监督预训练到有监督微调（SFT）及基于人类反馈的强化学习（RLHF）的转变过程。
- 分析模型缩放与行为：解释缩放定律、涌现能力（上下文学习、思维链）以及幻觉现象的概念。
- 分析仅编码器（BERT）、仅解码器（GPT）和编码器-解码器（T5）架构之间的结构差异。
- 解释三阶段训练过程：预训练（基础模型）、指令微调（SFT）与对齐（RLHF/PPO）。
- 比较主流大语言模型（如 GPT、Llama、Qwen、DeepSeek）在性能、缩放定律和架构创新方面的表现。
- 实现零样本与少样本提示策略，用于结构化数据提取与分类任务。
- 调整模型超参数（温度、Top P、惩罚项），以平衡创造性输出与确定性输出。
- 使用手动、自动以及零样本（“让我们一步步思考”）方法构建有效的思维链（CoT）提示。
- 分析并比较不同思维链变体：区分自一致性（Self-Consistency）、思维程序（PoT）、思维树（ToT）与思维图（GoT）架构。
课程 共 8 课时 · 预计 24.0h
课程
Lesson
This lesson explores the paradigm shift from task-specific AI to general-purpose Large Language Models, highlighting how scaling laws and the Transformer architecture enable emergent reasoning capabilities. Students will learn how the self-attention mechanism allows models to process data in parallel and why decoder-only architectures have become the standard for modern generative AI.
This lesson explores the evolution of Transformer architectures, highlighting why the industry has shifted toward Decoder-only models due to their superior scaling laws and generative capabilities. It also examines the foundational training pipeline, covering the transition from pre-training and Chinchilla optimality to modern instruction tuning and alignment techniques.
This lesson explores the evolution of prompting strategies, ranging from zero-shot instructions to few-shot demonstrations and Chain-of-Thought reasoning for complex logic. Students will learn how to improve model performance and reliability by using structural constraints, logical decomposition, and precise formatting to ensure outputs are suitable for programmatic use.
This lesson explores the evolution of reasoning in large language models, moving from linear Chain-of-Thought to advanced structured architectures like Tree-of-Thought and Graph-of-Thought. Students will learn how these frameworks, alongside techniques like Program of Thought and knowledge editing, enable models to perform deliberate planning, verification, and complex mathematical problem-solving.
This lesson explores the challenges of AI safety, focusing on the training paradox between model helpfulness and harmlessness, as well as the mechanics of adversarial jailbreak attacks. It also introduces statistical watermarking techniques, such as the KGW framework, which use vocabulary partitioning and logit bias to embed detectable, invisible signatures into AI-generated content.
This lesson explores the fundamental conflict in AI alignment between maintaining model helpfulness and ensuring harmlessness, highlighting how jailbreak attacks exploit this tension through role-playing and cognitive coercion. Students will learn how these vulnerabilities function and examine technical strategies, such as intent analysis and robust system prompting, to defend against prompt-based bypass attempts.
This lesson explores the evolution of Multi-modal Large Language Models (MLLMs) from vision-centric systems to integrated architectures capable of processing diverse sensory inputs like audio and 3D data. Students will learn how specialized encoders and projection bridges align non-textual signals into a unified semantic space, enabling the model to effectively reason across multiple modalities.
This lesson explores the development of autonomous GUI agents that use a tripartite architecture—planning, decision-making, and reflection—to interact with software interfaces. It further examines how Reinforcement Learning and RLHF enable these agents to adapt to dynamic environments while maintaining safety and reliability against potential risks.