Large Language Models (LLMs) are a class of AI models designed to understand, generate, and interact with human language. Built on deep learning techniques, LLMs are trained on massive text corpora and learn to capture complex linguistic patterns, semantics, and contextual relationships within language. These models are based on neural network architectures, particularly the Transformer, and are composed of layers of interconnected nodes. Each connection is associated with a parameter—a numeric value representing the model’s learned understanding of language. These parameters, or weights, are adjusted during training to optimize the model’s performance.

Types of LLMs:

• Representation Models (Encoder-Only)
  
• Generative Models (Decoder-Only)
  
• Encoder-Decoder Models
  

Applications of LLMs:

• Text generation: Creative writing, content generation.
  
• Text classification: Spam detection, sentiment analysis.
  
• Text clustering: Organizing unstructured data.
  
• Semantic search: Context-aware information retrieval.
  

• Function: Designed for understanding language rather than generating it.
  
• Architecture: Typically encoder-only.
  
• Model Type: Sequence-to-value.
  
• Use Case: Takes an input sequence and produces a classification, embedding, or other scalar output.
  

Example:
Input: “The weather today is great!” Output: 1 (positive sentiment)
These models are commonly used in:

• Text classification
• Sentiment analysis
• Named entity recognition
• Masked language modeling

Examples of representation models:

• BERT Large – Open-source, 340 million parameters
  BERT: Bidirectional Encoder Representations from Transformers
  

• Function: Focused on generating coherent and contextually relevant text.
  
• Architecture: Decoder-only.
  
• Model Type: Sequence-to-sequence.
  
• Use Case: Takes a text input (prompt) and generates a text output (completion).
  

Example:
Input: “What’s 1 + 1?” Output: “The answer is 2.”
These models are prompt-driven and require clear instructions to produce useful responses. They are not typically task-specific but can be fine-tuned for specific applications.

Examples of generative models:

• GPT-4 (OpenAI) – Proprietary, 1.75 trillion parameters
  GPT: Generative Pre-trained Transformer
  
  
• LLaMA 4 (Meta AI) – Open-source, 2 trillion parameters
  Llama: Large Language Model Meta AI
  
  
• Gemini (Google) – Proprietary, 27 billion parameters
  

Encoder-Decoder Models

• Function: Combine the strengths of both understanding and generation.
  
• Architecture: Full Transformer (encoder + decoder).
  
• Model Type: Sequence-to-sequence.
  
• Use Case: Effective for tasks like translation, summarization, and question answering.
  

Example – Masked Language Modeling:
Input: “LLMs can be used for [MASK], a form of [MASK].” Output: [MASK] = text generation [MASK] = generative AI
Examples of encoder-decoder models:

• T5 (Text-to-Text Transfer Transformer) – Google, 11 billion parameters

• Training (Pre-training)
  This is the initial phase where a model learns the structure and patterns of language from a large, diverse dataset.
  
  Characteristics:
  
  • Produces foundation models (also called base models).
    
  • Learns syntax, semantics, and general knowledge.
    
  • Focuses on next-token prediction (for generative models).
    
  • Utilizes unsupervised learning techniques.
    
  • Requires extensive computational resources (GPUs, TPUs, large memory).
    
  • Consumes vast amounts of data and time.
    
  
  
• Fine-tuning
  This step customizes the pre-trained model for specific tasks or domains.
  
  Characteristics:
  
  • Produces fine-tuned models.
    
  • Uses supervised learning with labeled datasets.
    
  • Trains on smaller datasets and consumes fewer resources.
    
  • Shorter training times compared to pre-training.
    
  • Enables models to follow instructions.
    

Training Techniques

• Supervised Learning: Uses labeled data for tasks like classification and regression.
  Example: Sentiment analysis with text labeled as positive or negative.
  
  
• Unsupervised Learning: Uses unlabeled data for discovering patterns.
  Example: Clustering similar documents or learning embeddings.
  
  
• Masked Language Modeling (MLM)
  • Key technique for training representation models like BERT.
    
  • Randomly masks tokens in input text.
    
  • The model learns to predict the masked tokens based on context.
    
  
  Example:
  Input: “[CLS] LLMs are large [MASK] models.” Output: language