Essential LLM Terms Explained

This article provides a clear, easy-to-understand overview of foundational terminology used in Large Language Models (LLMs) and modern AI systems. Each concept includes simple explanations and relatable examples to help non–data scientists quickly grasp how these technologies work.

1. Artificial Intelligence (AI)
   Software designed to perform tasks that normally require human intelligence.
   Example: Siri answering your questions.

2. Deep Learning
   A way for computers to learn from many examples using neural networks.
   Example: Recognizing cats in pictures.

3. Neural Network
   A layered system of connected nodes that mimic how the brain processes information.
   Example: A system that learns to play video games.

4. Transformer
   A neural network architecture designed to understand and generate language efficiently.
   Example: A chatbot holding a natural conversation.

5. Large Language Model (LLM)
   AI trained on enormous amounts of text to understand and generate human language.
   Example: Writing stories or answering questions.

6. Parameter
   Internal values a neural network adjusts while learning.
   Example: Tuning a guitar to get the right sound.

7. Positional Encoding
   Helps models understand word order.
   Example: Knowing “dog chases cat” differs from “cat chases dog.”

8. Self-Attention
   Allows models to focus on the most relevant words in a sentence.
   Example: Identifying “cake” as key in “I want to eat cake.”

9. Encoder
   The part of a Transformer that reads and understands input.
   Example: Understanding the question “What’s the weather?”

10. Decoder
    The part that generates output or responses.
    Example: “Today is sunny and warm.”

11. BERT
    A Transformer for understanding language (NLU).
    Example: Detecting whether a review is positive or negative.

12. GPT-3 / GPT-4
    Transformer models optimized for generating text.
    Example: Writing summaries or essays.

13. T5
    A model designed to both understand and generate text.
    Example: Translating English to Spanish.

14. Unsupervised Learning
    Learning patterns without labeled answers.
    Example: Grouping similar photos.

15. Foundation Model
    A large, general-purpose model that can be adapted to many tasks.
    Example: One model that writes emails, translates languages, and answers questions.

16. Zero-Shot Learning
    Performing new tasks without training examples.
    Example: Playing a new game with no practice.

17. Few-Shot Learning
    Learning from only a few examples.
    Example: Learning a song after hearing it twice.

18. Fine-Tuning
    Adjusting a pre-trained model for a focused task.
    Example: Teaching a model to answer dinosaur questions.

19. Prompt Tuning
    Improving results by phrasing prompts more effectively.
    Example: Asking “What’s the capital of France?” instead of “Where is Paris?”

20. Adapters
    Small add-on modules that give a model new capabilities without retraining the entire model.
    Example: Adding a new skill to a game character.

21. Natural Language Processing (NLP)
    The AI field focused on human language.
    Example: Chatbots or grammar checkers.

22. Natural Language Understanding (NLU)
    Understanding meaning in human language.
    Example: Knowing “I don’t like cats” is negative.

23. Natural Language Generation (NLG)
    Creating human-like text.
    Example: Writing stories.

24. Tokenization
    Splitting text into words or pieces (“tokens”).
    Example: “I have a dog” → “I”, “have”, “a”, “dog”.

25. Vocabulary
    The set of tokens a model knows.
    Example: Knowing “apple” and “banana” but not “kiwifruit.”

26. Pre-Training
    The initial learning phase using large text datasets.
    Example: Reading millions of documents to learn grammar and facts.

27. Transfer Learning
    Applying knowledge from one task to another.
    Example: Learning cats helps with learning dogs.

28. Sequence-to-Sequence (Seq2Seq)
    Models that turn one sequence into another.
    Example: Translating English to French.

29. Attention Mechanism
    The model highlights important inputs when generating outputs.
    Example: Prioritizing the word “pizza” in “I want to eat pizza.”

30. Beam Search
    A method to choose the most likely sequence of generated words.
    Example: Selecting the best next word in a sentence.

31. Perplexity
    Measures how well a model predicts text. Lower = better.
    Example: Fewer surprises in predicting next words.

32. In-Context Learning
    Models adapting behavior based on provided examples in a prompt.
    Example: Answering sports questions correctly after reading sports examples.

33. Data Augmentation
    Expanding training data by modifying existing samples.
    Example: Rephrasing sentences.

34. Bias
    Systematic errors caused by skewed training data.
    Example: Assuming all doctors are male.

35. Explainable AI (XAI)
    Making AI decisions understandable to humans.
    Example: Explaining why a model classifies a movie as a comedy.