Key concepts of LLMs explained. LLMs - From complex to clear.
Understanding Large Language Models (LLMs)
◈ Introduction to LLMs
LLMs are AI systems designed to generate human-like text by learning from extensive datasets. They're used for various tasks like translation and conversation. Popular LLMs include GPT (OpenAI), BERT (Google), and Mistral (Mistral AI).
➼ Core Concepts
1️⃣ Transformer Architecture: Uses attention mechanisms to process text, allowing the model to focus on different parts of input.
2️⃣ Tokenization: Breaks text into tokens (words or subwords) for easier processing.
3️⃣ Input Representations: Transforms text into vectorized formats to capture meaning and context.
4️⃣ Attention Mechanisms: Identifies relationships between input tokens to highlight relevant information and manage long-range dependencies.
➼ Transfer Learning and Fine-tuning
1️⃣ Transfer Learning: Transfers general knowledge from one domain to another, often freezing certain model layers to retain general features.
2️⃣ Fine-tuning: Adapts the model to specific tasks by adjusting specific layers and re-training parts of the model for targeted training.
➼ Types of LLMs
LLMs can be categorized into:
1️⃣ Encoder-only models: E.g., BERT, optimized for understanding and processing input data.
2️⃣ Decoder-only models: E.g., GPT, designed for generating text outputs.
3️⃣ Encoder-decoder models: E.g., T5, handle tasks involving both input understanding and output generation, useful for translation and summarization.
➼ Preprocessing Techniques
1️⃣ Text Normalization: Standardizes text by adjusting casing and removing punctuation.
2️⃣ Stop Words: Removes commonly used words to focus on meaningful content.
3️⃣ Lemmatization and Stemming: Reduces words to their root form for efficiency.
➼ Applications of LLMs
LLMs are transforming industries, projected to reach a $1.3 trillion market by 2032 for generative AI solutions like ChatGPT and Google Bard. They enhance search functionalities, customer support, virtual assistants, code development, keyword analysis, and more, driving efficiency and insights across domains.
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