- 大语言模型实用指南(影印版)(英文版)
- - 作者：(美)杰伊·阿拉玛//马尔滕·葛鲁顿第斯特|责编:张烨
  - 出版社：东南大学
  - ISBN：9787576617665
  - 出版日期：2025/02/01
  - 页数：403
- 售价：74.4

内容大纲
在过去的几年，AI获得了令人惊讶的新语言能力。在深度学习快速发展的推动下，语言AI系统比以往任何时候都能更好地编写和理解文本。这一趋势正在催生新功能、新产品，甚至新的行业。通过本书的可视化教育方式，读者将学习到现在使用这些功能所需的实用工具和概念。
你将了解如何将预训练的大语言模型用于文案撰写和摘要生成等应用场景，创建超越关键字匹配的语义搜索系统，以及使用现有库和预训练的模型进行文本分类、搜索和聚类。
作者介绍
目录
Preface
Part I.  Understanding Language Models
  1. An Introduction to Large Language Models
    What Is Language AI?
    A Recent History of Language AI
      Representing Language as a Bag-of-Words
      Better Representations with Dense Vector Embeddings
      Types of Embeddings
      Encoding and Decoding Context with Attention
      Attention Is All You Need
      Representation Models: Encoder-Only Models
      Generative Models: Decoder-Only Models
      The Year of Generative AI
    The Moving Definition of a "Large Language Model"
    The Training Paradigm of Large Language Models
    Large Language Model Applications: What Makes Them So Useful?
    Responsible LLM Development and Usage
    Limited Resources Are All You Need
    Interfacing with Large Language Models
      Proprietary, Private Models
      Open Models
      Open Source Frameworks
    Generating Your First Text
    Summary
  2. Tokens and Embeddings
    LLM Tokenization
      How Tokenizers Prepare the Inputs to the Language Model
      Downloading and Running an LLM
    How Does the Tokenizer Break Down Text?
    Word Versus Subword Versus Character Versus Byte Tokens
      Comparing Trained LLM Tokenizers
    Tokenizer Properties
    Token Embeddings
    A Language Model Holds Embeddings for the Vocabulary of Its Tokenizer
      Creating Contextualized Word Embeddings with Language Models
    Text Embeddings (for Sentences and Whole Documents)
    Word Embeddings Beyond LLMs
    Using pretrained Word Embeddings
    The Word2vec Algorithm and Contrastive Training
    Embeddings for Recommendation Systems
    Recommending Songs by Embeddings
    Training a Song Embedding Model
    Summary
  3. Looking Inside Large Language Models
    An Overview of Transformer Models
    The Inputs and Outputs of a Trained Transformer LLM
    The Components of the Forward Pass
      Choosing a Single Token from the Probability Distribution (Sampling/Decoding)
    Parallel Token Processing and Context Size
    Speeding Up Generation by Caching Keys and Values

    Inside the Transformer Block
    Recent Improvements to the Transformer Architecture
    More Efficient Attention
    The Transformer Block
    Positional Embeddings (ROPE)
    Other Architectural Experiments and Improvements
    Summary
Part II.  Using Pretrained Language Models
  4. Text Classification
    The Sentiment of Movie Reviews
    Text Classification with Representation Models
    Model Selection
    Using a Task-Specific Model
    Classification Tasks That Leverage Embeddings
    Supervised Classification
    What If We Do Not Have Labeled Data?
    Text Classification with Generative Models
    Using the Text-to-Text Transfer Transformer
    ChatGPT for Classification
    Summary
  5. Text Clustering and Topic Modeling
    ArXiv's Articles: Computation and Language
    A Common Pipeline for Text Clustering
    Embedding Documents
    Reducing the Dimensionality of Embeddings
    Cluster the Reduced Embeddings
    Inspecting the Clusters
    From Text Clustering to Topic Modeling
    BERTopic: A Modular Topic Modeling Framework
    Adding a Special Lego Block
    The Text Generation Lego Block
    Summary
  6. Prompt Engineering
    Using Text Generation Models
    Choosing a Text Generation Model
    Loading a Text Generation Model
    Controlling Model Output
    Intro to Prompt Engineering
    The Basic Ingredients of a Prompt
    Instruction-Based Prompting
    Advanced Prompt Engineering
    The Potential Complexity of a Prompt
    In-Context Learning: Providing Examples
    Chain Prompting: Breaking up the Problem
    Reasoning with Generative Models
    Chain-of-Thought: Think Before Answering
    Self-Consistency: Sampling Outputs
    Tree-of-Thought: Exploring Intermediate Steps
    Output Verification
    Providing Examples

    Grammar: Constrained Sampling
    Summary
  7. Advanced Text Generation Techniques and Tools
    Model I/O: Loading Quantized Models with LangChain
    Chains: Extending the Capabilities of LLMs
    A Single Link in the Chain: Prompt Template
    A Chain with Multiple Prompts
    Memory: Helping LLMs to Remember Conversations
    Conversation Buffer
    Windowed Conversation Buffer
    Conversation Summary
    Agents: Creating a System of LLMs
    The Driving Power Behind Agents: Step-by-step Reasoning
    ReAct in LangChain
    Summary
  8. Semantic Search and Retrieval-Augmented Generation
    Overview of Semantic Search and RAG
    Semantic Search with Language Models
    Dense Retrieval
    Reranking
    Retrieval Evaluation Metrics
    Retrieval-Augmented Generation (RAG)
    From Search to RAG
    Example: Grounded Generation with an LLM API
    Example: RAG with Local Models
    Advanced RAG Techniques
    RAG Evaluation
    Summary
  9. Multimoflal Large Language Models
    Transformers for Vision
    Multimodal Embedding Models
    CLIP: Connecting Text and Images
    How Can CLIP Generate Multimodal Embeddings?
    OpenCLIP
    Making Text Generation Models Multimodal
    BLIP-2: Bridging the Modality Gap
    Preprocessing Multimodal Inputs
    Use Case 1: Image Captioning
    Use Case 2: Multimodal Chat-Based Prompting
    Summary
Part III.  Training and Fine-Tuning Language Models
  10. Creating Text Embedding Models
    Embedding Models
    What Is Contrastive Learning?
    SBERT
    Creating an Embedding Model
      Generating Contrastive Examples
      Train Model
      In-Depth Evaluation
      Loss Functions

    Fine-Tuning an Embedding Model
      Supervised
      Augmented SBERT
    Unsupervised Learning
      Transformer-Based Sequential Denoising Auto-Encoder
      Using TSDAE for Domain Adaptation
    Summary
  11. Fine-Tuning Representation Models for Classification
    Supervised Classification
      Fine-Tuning a Pretrained BERT Model
      Freezing Layers
    Few-Shot Classification
      SetFit: Efficient Fine-Tuning with Few Training Examples
      Fine-Tuning for Few-Shot Classification
    Continued Pretraining with Masked Language Modeling
    Named-Entity Recognition
      Preparing Data for Named-Entity Recognition
      Fine-Tuning for Named-Entity Recognition
    Summary
  12. Fine-Tuning Generation Models
    The Three LLM Training Steps: Pretraining, Supervised Fine-Tuning, and
      Preference Tuning
    Supervised Fine-Tuning (SFT)
      Full Fine-Tuning
      Parameter-Efficient Fine-Tuning (PEFT)
    Instruction Tuning with QLoRA
      Templating Instruction Data
      Model Quantization
      LoRA Configuration
      Training Configuration
      Training
      Merge Weights
    Evaluating Generative Models
      Word-Level Metrics
      Benchmarks
      Leaderboards
      Automated Evaluation
      Human Evaluation
    Preference-Tuning / Alignment / RLHF
    Automating Preference Evaluation Using Reward Models
      The Inputs and Outputs of a Reward Model
      Training a Reward Model
      Training No Reward Model
    Preference Tuning with DPO
      Templating Alignment Data
      Model Quantization
      Training Configuration
      Training
    Summary
Afterword

Index

内容大纲

作者介绍

目录

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