LLM Cheatsheet: Top 15 LLM Terms You Need to Know in 2024
Large Language Models (LLMs) are revolutionizing the way we interact with technology. But with all this innovation comes a new vocabulary! Fear not, fellow AI enthusiasts, for this blog is your decoder ring to the fascinating world of LLM lingo. Let's dive into some essential terms:
LLM Cheatsheet: Top 15 LLM Terms You Need to Know in 2024
Transformers: Imagine a powerful language processing architecture. That's a Transformer! It analyzes relationships between words in text, crucial for LLMs to understand and generate language
Token: Think of a sentence as a train, and words as individual carriages. Tokens are the basic units of text an LLM processes, like words or sub-words.
Chunking: Similar to grouping train cars, chunking involves breaking down text into smaller, manageable segments for the LLM to analyze
Indexing: It's like creating a library catalog for the massive datasets LLMs train on. Indexing allows for efficient retrieval of specific information.
Embedding: Imagine capturing the essence of a word in a numerical code. Embeddings represent words in a way that lets the LLM understand their relationships to each other.
Vector Search: Think of finding a book in a library based on its topic. Vector search helps LLMs find similar information within their vast datasets using embeddings.
Vector Database: This specialized database stores the numerical codes (embeddings) generated during the embedding process, allowing for efficient vector search.
Artificial General Intelligence (AGI): The ultimate goal of AI research - creating machines that can think and learn like humans. LLMs are a significant step in this direction.
LLM Agent: Imagine an LLM specifically trained for a particular task, like writing different kinds of creative content or answering your questions in a specific domain. This is an LLM Agent.
MOE (Mixture of Experts): Think of a team of specialists collaborating. MOE allows an LLM to leverage multiple smaller expert models for improved performance on specific tasks.
Shot Learning: This refers to how much instruction an LLM needs to learn a new task.
Zero-Shot: No examples provided, the LLM relies on its existing knowledge.
One-Shot: Just one example is given to guide the LLM.
N-Shot: Multiple examples are provided for the LLM to learn from.
Parameter-Efficient Fine-Tuning: LLMs can be huge! This technique allows for training a smaller model on top of a larger one, focusing on a specific task while keeping resource usage in check.
RAG (Retrieval-Augmented Generation): Imagine an LLM that not only generates text but also retrieves relevant information from external sources to enhance its response. That's RAG!
Prompt Engineering: The art of crafting clear and concise instructions for the LLM to achieve the desired outcome. Think of it as giving the LLM a specific question to answer.
Quantized LoRA (Low-Rank Adapters): A technique for compressing large LLM models, making them smaller and faster to run on devices with limited resources.
This list just scratches the surface of LLM lingo! As the field continues to evolve, so will the vocabulary. But with these core terms in your arsenal, you'll be well on your way to navigating the exciting world of Large Language Models.
