update large language model

docs/大型语言模型与生成模型/CS324.en.md

@@ -0,0 +1,27 @@
+# Stanford CS324: Large Language Models
+
+## Course Introduction
+
+- **University**: Stanford University
+- **Prerequisites**: None specified, but familiarity with natural language processing and machine learning is recommended.
+- **Course Difficulty**: 🌟🌟🌟🌟🌟
+- **Course Website**: [CS324: Large Language Models](https://stanford-cs324.github.io/winter2022/)
+
+"CS324: Large Language Models" is a graduate-level course at Stanford University that explores the fundamentals, theory, ethics, and system aspects of large language models (LLMs). The course also offers hands-on experience in evaluating and building these models.
+
+Massive pre-trained LLMs have revolutionized the field of natural language processing (NLP), enabling state-of-the-art performance across numerous tasks and demonstrating the ability to generate fluent text and perform few-shot learning. However, these models are challenging to interpret and introduce new ethical and scalability concerns. This course provides students with a comprehensive understanding of LLMs and practical exposure through projects and paper discussions.
+
+### Coursework
+
+1. **Paper Reviews and Discussions** (20%)
+   - Write reviews for assigned papers.
+   - Participate in at least two student panels to lead discussions.
+
+2. **Projects** (2 × 40% = 80%)
+   - **Project 1**: Evaluate the capabilities and risks of language models (e.g., GPT-3).
+   - **Project 2**: Build and improve language models using tools like BERT-base.
+
+
+---
+
+

docs/大型语言模型与生成模型/CS324.md

@@ -0,0 +1,28 @@
+# Stanford CS324: Large Language Models
+
+## 课程简介
+
+- **所属大学**: 斯坦福大学
+- **先修要求**: 未指定，但建议具备自然语言处理和机器学习的基础知识。
+- **课程难度**: 🌟🌟🌟🌟🌟
+- **课程网站**: [CS324: 大型语言模型](https://stanford-cs324.github.io/winter2022/)
+
+《CS324: 大型语言模型》是斯坦福大学开设的一门研究生课程，探讨大型语言模型（LLMs）的基础理论、伦理问题、系统架构等方面，并提供评估和构建这些模型的实践机会。
+
+大规模预训练的 LLMs 革新了自然语言处理（NLP）领域，实现了多个任务的最先进性能，并展现了生成流畅文本和少样本学习的能力。然而，这些模型难以理解，同时带来了新的伦理和可扩展性挑战。本课程旨在让学生全面理解 LLMs，并通过项目和论文讨论获得实践经验。
+
+### 课程作业
+
+1. **论文评审与讨论**（20%）
+   - 撰写所分配论文的评审。
+   - 至少参与两次学生小组讨论并主导讨论。
+
+2. **项目**（2 × 40% = 80%）
+   - **项目 1**: 评估语言模型（例如 GPT-3）的能力与风险。
+   - **项目 2**: 使用 BERT-base 等工具构建和改进语言模型。
+
+项目需以 1-2 人小组完成，使用清晰的格式编排，并以 PDF 提交。截止时间为每次晚上 11:00 PST，通过 Gradescope 提交。
+
+
+---
+

docs/大语言与深度生成模型/CS194.en.md

@@ -1,24 +0,0 @@
-# UC Berkeley CS 194/294-267: Understanding Large Language Models: Foundations and Safety
-
-## Course Introduction
-
-- **University**: UC Berkeley
-- **Prerequisites**: CS 182/282A Deep Neural Networks or equivalent, with hands-on deep learning experience.
-- **Course Difficulty**: 🌟🌟🌟🌟🌟🌟
-- **Course Website**: [Understanding Large Language Models](http://rdi.berkeley.edu/understanding_llms/s24)
-- **Lecture Video**：<https://www.youtube.com/playlist?list=PLJ66BAXN6D8H_gRQJGjmbnS5qCWoxJNfe>
-
-"Understanding Large Language Models: Foundations and Safety" is a Spring 2024 course at UC Berkeley, co-taught by **Professor Dawn Song** and **Dan Hendrycks**, with **Yu Gai** as the GSI. This course explores the foundational principles, interpretability, scaling laws, and risks associated with large language models (LLMs) such as ChatGPT.
-
-The course provides a rigorous introduction to LLMs, discussing their emergence, limitations, and potential risks, as well as methods for safer and more beneficial applications. Topics covered include:
-
-- Foundations of LLMs
-- Interpretability
-- Scaling laws
-- Adversarial robustness
-- AI alignment and governance
-- Privacy, watermarking, and Trojans
-- Agency, emergence, reasoning, and mathematics
-- Evaluation and benchmarking
-
-

docs/大语言与深度生成模型/CS194.md

@@ -1,23 +0,0 @@
-# UC Berkeley CS 194/294-267： Understanding Large Language Models: Foundations and Safety
-
-## 课程简介
-
-- **所属大学**：UC Berkeley
-- **先修要求**：CS 182/282A 深度神经网络课程或等效课程，并具有深度学习的实际操作经验。
-- **课程难度**：🌟🌟🌟🌟🌟🌟
-- **课程网站**：<http://rdi.berkeley.edu/understanding_llms/s24>
-- **课程视频**：<https://www.youtube.com/playlist?list=PLJ66BAXN6D8H_gRQJGjmbnS5qCWoxJNfe>
-
-《理解大型语言模型：基础与安全》是 UC Berkeley 在 2024 年春季开设的一门课程，由 **Dawn Song 教授** 和 **Dan Hendrycks** 联合授课，助教为 **Yu Gai**。本课程重点探讨大型语言模型（如 ChatGPT）的基础原理、可解释性、扩展定律以及相关风险。
-
-课程内容包括：
-
-- 大型语言模型的基础
-- 可解释性
-- 扩展定律
-- 对抗鲁棒性
-- 人工智能对齐与治理
-- 隐私、水印与木马
-- 代理性、涌现性、推理与数学
-- 评估与基准测试
-

mkdocs.yml

@@ -280,4 +280,8 @@ nav:
       - "Columbia STAT 8201: Deep Generative Models": "机器学习进阶/STAT8201.md"
       - "U Toronto STA 4273 Winter 2021: Minimizing Expectations": "机器学习进阶/STA4273.md"
       - "Stanford STATS214 / CS229M: Machine Learning Theory": "机器学习进阶/CS229M.md"
+  - 大型语言模型与生成模型:
+    - "CMU CS11-667: Large Language Models Methods and Application": "大型语言模型与生成模型/CS11-667.md"
+    - "Stanford CS324: Large Language Models" : "大型语言模型与生成模型/CS324.md"
+    - "UCB CS194/294-267: Understanding Large Language Models": "大型语言模型与生成模型/CS194-267.md"
   - 后记: "后记.md"