👓 Theory Chapter ​

This chapter is divided into six sections, namely the overview of large language models, Transformer, tokenization, BERT model, fine-tuning of GPT model, and T5 model. It mainly introduces the basic framework structure, code implementation, and specific usage methods.

📔 Table of Contents ​

• LLM Overview
• Transformer
• Tokenizer
• BERT
• GPT Series
• T5

👉LLM Overview ​

This section provides an overview of large models from the following aspects:

• Model Introduction: First, introduce the basic information about large models, their application areas, and significance.
• Model Structure: Describe the structure of large models, including hierarchical structure and connection methods. Present the model's structure through words, diagrams, or formulas to help readers understand the components and relationships between them.
• Model Training: Explain the training methods and datasets used for large models. Detailed descriptions of algorithms, hyperparameter selection, and data preparation during training are provided. Additionally, challenges encountered during training and solutions are discussed, as well as evaluation metrics for training results.
• Model Performance: Showcase the performance of large models on various tasks or benchmark datasets. This section lists some tasks or benchmark datasets and presents the performance of large models in these tasks, such as accuracy and recall rates. It also compares large models with other models to demonstrate their strengths and weaknesses.
• Limitations and Future Work: Discuss the limitations of large models and potential improvements. Address any shortcomings in specific tasks or scenarios where large models perform poorly and propose ideas and directions for improvement. At the same time, explore future research directions and potential applications of large models in other fields.

👉Transformer ​

This section presents specific content from the following aspects:

• Background and Significance of Transformer Models:

  • This section introduces the background and significance of Transformer models, as well as their applications in natural language processing and other fields.

• Problems with Traditional Sequence Models (e.g., Recurrent Neural Networks):

  • This section highlights the problems with traditional sequence models and introduces the advantages of Transformer models.

• Attention Mechanism:

  • Basic Principles and Roles of Attention Mechanism: This section explains the basic principles and roles of attention mechanism.
  • Concepts of Self-Attention and Multi-Head Attention: This section introduces the concepts of self-attention and multi-head attention and emphasizes their key roles in the Transformer model.

• Structure of Transformer Models:

  • This section describes the overall structure of Transformer models, including encoders and decoders. It provides detailed information on the components of encoders and decoders, including multi-layer self-attention and fully connected layers, as well as the flow of input and output data.

• Self-Attention Mechanism:

  • This section explains how self-attention mechanisms work, including the calculation of attention weights and generation of context vectors. It also discusses the advantages of self-attention mechanisms over traditional sequence models, such as capturing long-distance dependencies.

• Multi-Head Attention: -This section introduces the concept and role of multi-head attention, explaining how it parallelly computes multiple attention weights and context vectors.

• Positional Encoding:

  • This section explains the role and necessity of positional encoding, describing its implementation using sine and cosine functions for encoding positions.

• Model Training and Inference:

  • This section explains the differences between training and inference stages for Transformer models, mentioning loss functions, optimization algorithms, and learning rate scheduling methods used during training.

• Improvements and Extensions to Transformer Models:

  • This section introduces some improvements and extensions to Transformer models, such as BERT, GPT, etc. It emphasizes the performance improvements and application values these improved models bring to different tasks.

👉Tokenizer ​

Tokenizer is the smallest unit that a computer can understand and process in NLP tasks. This section will introduce the main content from several different types of tokenizers, showing that there are different tokenization effects depending on the tokenizer used.

The framework structure of this chapter is as follows:

• Introduction to Tokenizer background and role, as well as its importance in natural language processing.
• Emphasize the key role of Tokenizer in text preprocessing, including tokenization, tokenization, lemmatization, etc.
• Basic concepts:
  • Define the basic concepts of Tokenizer, including Token, vocabulary, sequence, etc.
  • Explain the concept and process of Tokenization, which is to divide the text into a series of unbreakable units.
• Common Tokenizer algorithms and methods:
  • Introduce rule-based Tokenizer algorithms, such as simple segmentation methods based on spaces and punctuation marks.
  • Describe machine learning-based Tokenizer methods, such as statistical segmentation and maximum matching methods.
  • Mention deep learning-based Tokenizer models, such as segmentation models based on recurrent neural networks or transformers.
• Tokenization standards and language relevance:
  • Explain the importance of tokenization standards, such as the Modern Chinese Dictionary and the Greenspan standard for English.
  • Emphasize the characteristics and challenges of tokenization in different languages, such as the differences between Chinese and English segmentation methods.
• Word form recovery and part-of-speech tagging:
  • Introduce the role and method of word form recovery, which is to restore words to their original forms with different forms.
  • Briefly explain the concept and role of part-of-speech tagging, which is to tag each word with its part of speech.
• Common Tokenizer libraries and tools:
  • Introduce some common open-source Tokenizer libraries and tools, such as NLTK, spaCy, BERT Tokenizer, etc.
  • Emphasize the application scenarios and advantages of these tools in different tasks and language processing.

👉BERT ​

The following is an overview of the framework structure of BERT (Bidirectional Encoder Representations from Transformers) model:

• Introduction to the background and significance of BERT model, as well as its importance in the field of natural language processing.
• Emphasize the outstanding performance and wide application of BERT model in language understanding tasks.
• The structure of BERT model:
  • Describe the overall structure of BERT model, including the encoder and pre-trained task objectives.
  • Detailedly introduce the encoder part of BERT model, which is a stack of multi-layered transformer encoders.
  • Explain that the input to the encoder is processed text sequences using WordPiece or other tokenization methods.
• Pretraining of BERT model:
  • Explain the pretraining stage of BERT model, which is self-supervised learning on large unannotated text datasets.
  • Emphasize the pretraining task objectives, such as Masked Language Model (MLM) and Next Sentence Prediction (NSP).
  • Mention the large corpus and training strategies required for pretraining, such as batch training and dynamic masking.
• Fine-tuning of BERT model:
  • Explain the fine-tuning stage of BERT model, which is supervised learning on labeled data for specific tasks.
  • Mention common fine-tuning tasks, such as text classification, named entity recognition, and question answering.
  • Emphasize the adjustment of model architecture and task-specific output layers during fine-tuning.

👉GPT Series ​

The following is an overview of the framework structure of GPT-2 (Generative Pre-trained Transformer 2) model:

• Introduction to the background and significance of GPT-2 model, as well as its importance in the field of natural language processing.
• Emphasize the outstanding performance and wide application of GPT-2 model in language generation tasks.
• The structure of GPT-2 model:
  • Describe the overall structure of GPT-2 model, including the stacking of multi-layered transformer decoders.
  • Explain that GPT-2 model is a unidirectional language model that generates text sequences through autoregressive generation.
• The training process of GPT-2 model:
  • Detailedly introduce the training process of GPT-2 model, which includes pretraining and fine-tuning two stages.
  • Explain the task used in the pretraining stage, such as Masked Language Model (MLM) and Next Sentence Prediction (NSP).
  • Emphasize that GPT-2 model uses unsupervised learning and self-learning on large unannotated text datasets.
• The generating ability of GPT-2 model:
  • Explain that GPT-2 model can generate coherent and contextually relevant text sequences through language representation learned during pretraining.
  • Mention the excellent performance and wide application of GPT-2 model in text generation tasks, such as dialogue generation, article creation, etc.
• The application areas of GPT-2 model:
  • Introduce the applications of GPT-2 model in natural language processing tasks, such as machine translation, text summarization, and question answering systems.
  • Emphasize the advantages of GPT-2 model in generating long texts and handling complex contexts.

👉T5 Series ​

This section introduces the T5 model from the following aspects:

• Overview of T5 Model
• Differences between T5 Model and BERT
• Architecture of T5 Model
• Training Process of T5 Model
• Application Scenarios of T5 Model
• Advantages and Disadvantages of T5 Model
• Future Development Directions of T5 Model