🤖 Chapter 1: What is LLM

Definition, History, and Future of Large Language Models

📖 Reading Time: 25-30 min 📊 Difficulty: Beginner 💻 Code Examples: 5 📝 Exercises: 3

Introduction

Since 2023, with the advent of ChatGPT, AI technology has rapidly permeated into general society. The technology behind ChatGPT is the Large Language Model (LLM).

In this chapter, we will learn what LLMs are, the history that led to their current form, and what representative models exist.

1.1 Definition of LLM

What is a Large Language Model (LLM)

A Large Language Model (LLM) is a deep learning model trained on massive amounts of text data that performs natural language understanding and generation.

📌 Key Characteristics of LLMs

Basic Structure of LLMs

Most modern LLMs are based on the Transformer architecture. Transformer is a revolutionary neural network structure announced by Google in 2017.

graph TD A[Input Text] --> B[Tokenization] B --> C[Embedding Layer] C --> D[Transformer Layer x N] D --> E[Output Layer] E --> F[Predicted Text] style A fill:#e3f2fd style B fill:#fff3e0 style C fill:#f3e5f5 style D fill:#e8f5e9 style E fill:#fce4ec style F fill:#e3f2fd

1.2 History of LLMs

Evolution of Language Models

Language models have a long history, but they have developed rapidly since 2018.

timeline title Evolution of LLMs 2017 : Transformer emerges (Vaswani et al.) 2018 : BERT (Google), GPT-1 (OpenAI) 2019 : GPT-2 (OpenAI), T5 (Google) 2020 : GPT-3 (175 billion parameters) 2021 : Codex (GitHub Copilot) 2022 : ChatGPT released (GPT-3.5 based) 2023 : GPT-4, Claude, LLaMA, Gemini 2024 : GPT-4 Turbo, Claude 3, LLaMA 3

Major Milestones

2017: Transformer

The Transformer proposed in Google's paper "Attention is All You Need" became the foundational architecture for LLMs.

2018: BERT (Bidirectional Encoder Representations from Transformers)

A bidirectional language model announced by Google. Its ability to consider context from both directions was groundbreaking.

2018: GPT-1 (Generative Pre-trained Transformer)

A generative language model announced by OpenAI. It established the pre-training + fine-tuning approach.

2020: GPT-3

The third generation of the GPT series. A dramatic increase in parameters enabled Few-Shot Learning.

2022: ChatGPT

A chatbot based on GPT-3.5 and tuned with human feedback. It became the catalyst for the democratization of AI technology.

2023: GPT-4

OpenAI's latest model (at the time of writing). It supports multimodal (text + image) capabilities.

1.3 Representative LLM Models

Comparison of Major LLMs

Model Developer Parameters Features Availability
GPT-4 OpenAI Undisclosed (estimated 1T+) Multimodal, high accuracy Via API
Claude 3 Anthropic Undisclosed Long-text understanding, safety-focused Via API
Gemini Google Undisclosed Multimodal, integrated Via API
LLaMA 3 Meta 8B, 70B, 405B Open source, high efficiency Fully open
Mistral Mistral AI 7B, 8x7B Small high-performance, MoE Open source

💡 Parameter Notation

Details of Each Model

GPT-4 (OpenAI)

Claude 3 (Anthropic)

Gemini (Google)

LLaMA 3 (Meta)

1.4 Tokenization Mechanism

What are Tokens

LLMs do not process strings directly but split them into units called tokens. Tokens can be parts of words, entire words, or punctuation marks.

🔍 Tokenization Example

Input Text: "ChatGPT is an amazing AI"

Token Split: ["Chat", "G", "PT", " is", " an", " amazing", " AI"]

→ 7 tokens

Main Tokenization Methods

1. BPE (Byte Pair Encoding)

2. WordPiece

3. SentencePiece

Python Code Example for Tokenization

# Tokenization using Hugging Face transformers
from transformers import AutoTokenizer

# Load GPT-2 tokenizer
tokenizer = AutoTokenizer.from_pretrained("gpt2")

# Tokenize text
text = "ChatGPT is an amazing AI"
tokens = tokenizer.tokenize(text)
print("Tokens:", tokens)
# Example output: ['Chat', 'G', 'PT', ' is', ' an', ' amazing', ' AI']

# Convert to token IDs
token_ids = tokenizer.encode(text)
print("Token IDs:", token_ids)

# Check number of tokens
print(f"Number of tokens: {len(token_ids)}")

⚠️ Importance of Token Count

Many LLM APIs charge based on token count. Additionally, models have maximum token limits (context length).

1.5 Fundamentals of Transformer Architecture

Basic Structure of Transformer

Transformer consists of Encoder and Decoder, but most LLMs adopt a Decoder-Only architecture.

graph TD A[Input Tokens] --> B[Embedding + Position Encoding] B --> C[Multi-Head Self-Attention] C --> D[Add & Norm] D --> E[Feed-Forward Network] E --> F[Add & Norm] F --> G[Next Layer or Output] style A fill:#e3f2fd style C fill:#fff3e0 style E fill:#f3e5f5 style G fill:#e8f5e9

Main Components

1. Self-Attention

A mechanism where each word in a sentence learns its relationship with all other words.

🔍 Self-Attention Example

Sentence: "The cat ate the fish"

Focusing on "ate":

→ The model automatically learns grammatical structure

2. Multi-Head Attention

Computes attention from multiple different perspectives (heads) and processes them in parallel.

3. Position Encoding

Transformer requires explicit word order information due to parallel processing.

4. Feed-Forward Network

A fully connected layer that independently transforms the representation of each token.

Decoder-Only vs Encoder-Decoder

Architecture Representative Models Features Main Use Cases
Decoder-Only GPT-3, GPT-4, LLaMA Autoregressive generation Text generation, chat
Encoder-Only BERT Bidirectional understanding Text classification, NER
Encoder-Decoder T5, BART Input→Output transformation Translation, summarization

1.6 LLM Use Cases

Main Application Areas

1. Content Generation

2. Code Generation and Assistance

3. Question Answering and Customer Support

4. Translation and Summarization

5. Educational Support

Try Using LLM: Simple Code Example

# Text generation with GPT-2 using Hugging Face transformers
from transformers import pipeline

# Create text generation pipeline
generator = pipeline('text-generation', model='gpt2')

# Generate text with a prompt
prompt = "When thinking about the future of artificial intelligence"
result = generator(
    prompt,
    max_length=100,
    num_return_sequences=1,
    temperature=0.7
)

print(result[0]['generated_text'])

💡 Parameter Explanation

1.7 Limitations and Challenges of LLMs

Main Challenges

1. Hallucination

LLMs can generate non-existent information in a plausible manner.

⚠️ Example of Hallucination

Question: "Who won the 2024 Nobel Prize in Physics?"

Example of Incorrect Answer: "Dr. Taro Yamada won for his research in quantum computing"

→ Models cannot say "I don't know" and may generate plausible lies

2. Bias and Fairness

3. Knowledge Cutoff

4. Computational Cost and Energy

5. Privacy and Security

Countermeasures and Mitigation Strategies

1.8 Future of LLMs

Future Development Directions

1. Multimodal AI

Models that integrate understanding and generation of not only text but also images, audio, and video.

2. More Efficient Models

Development of smaller yet high-performance models.

3. Agent-type AI

AI that can use tools, make plans, and take actions.

4. Personalization

AI assistants optimized for individuals.

5. Open Source Movement

Trend toward more models being released as open source.

Summary

In this chapter, we learned the fundamentals of Large Language Models (LLMs).

📌 Key Points

Exercises

📝 Exercise 1: Basic Knowledge Check

Question: Answer the following questions.

  1. What does "large-scale" in LLM refer to?
  2. List two main advantages of the Transformer architecture.
  3. Explain the difference between Decoder-Only and Encoder-Only models.

📝 Exercise 2: Tokenization Practice

Task: Run the following code and compare token counts for different texts.

from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("gpt2")

texts = [
    "Hello",
    "Bonjour",
    "Artificial Intelligence is amazing",
    "人工知能は素晴らしい"
]

for text in texts:
    tokens = tokenizer.encode(text)
    print(f"'{text}' → {len(tokens)} tokens")

Analysis: Are there differences in token counts between different languages? Consider the reasons.

📝 Exercise 3: Model Comparison

Task: Choose one from GPT-4, Claude, or LLaMA and research the following:

Advanced: Read the official documentation of the chosen model and summarize technical details.

Next Chapter

In the next chapter, we will study the Transformer architecture, the core technology of LLMs, in detail. You will understand the mechanisms of Self-Attention, Multi-Head Attention, position encoding, etc., and experience them with working code.

← Series Overview Chapter 2: Transformer Architecture (Coming Soon)

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