ChatGPT Large Language Models Glossary

Welcome to the definitive ChatGPT and Large Language Models (LLMs) Glossary, your essential guide to navigating the intricate landscape of advanced language technologies.

Whether you’re an aspiring researcher diving into the depths of machine learning, a developer integrating cutting-edge AI into your applications, or simply a tech enthusiast fascinated by the progress of natural language processing, this glossary is crafted to offer you a clear and concise understanding of the pivotal terms and concepts that underpin ChatGPT and LLMs.

As we stand on the brink of a new era in artificial intelligence, where machines understand and generate human-like text with unprecedented accuracy, the importance of grasping these concepts cannot be overstated. Understanding the terms in this glossary is crucial for understanding how large language models like ChatGPT function.

A

• Attention Mechanism
• Autoregressive Model
• Activation Function
• Adversarial Examples

B

• BERT (Bidirectional Encoder Representations from Transformers)
• Bias
• Backpropagation

C

• Chatbot
• Contextual Embeddings
• Corpus
• Cross-Entropy Loss
• Conditional Language Generation

D

• Data Tokenization
• Dataset
• Decoding Strategies
• Deep Learning
• Dialogue Systems

E

• Embedding Layer
• Encoder-Decoder Architecture
• Epoch
• Entity Recognition

F

• FAQ Generation
• Feature Extraction
• Fine-tuning

G

• GPT (Generative Pre-trained Transformer)
• GAN (Generative Adversarial Network)
• Graph Neural Networks (GNN)
• Gradient Clipping

H

• Hashing Vectorizer
• Hyperparameter Tuning
• Heuristic

I

• Inference
• Iterative Training
• Intent Recognition

J

• Jargon
• Jupyter Notebooks
• Joint Embedding

K

• Knowledge Distillation
• Knowledge Base
• K-nearest Neighbors (in the context of NLP tasks)

L

• Language Model
• Large Language Model (LLM)
• Latent Representation
• Loss Function
• Learning Rate
• Lexical Ambiguity

M

• Machine Learning
• Masked Language Model
• Model Architecture
• Multimodal Models
• Multitask Learning

N

• Natural Language Generation (NLG)
• Natural Language Processing (NLP)
• Natural Language Understanding (NLU)
• Neural Network
• Normalization
• Named Entity Recognition (NER)

O

• OpenAI
• Out-of-Vocabulary (OOV) Words
• Overfitting
• Ontology

P

• Perplexity
• Pre-training
• Prompt Engineering
• Prompt Design
• Polyglot
• Pretext Task

Q

• Q-learning
• Quantization
• Query Understanding
• Query Generation
• Question Answering

R

• Recurrent Neural Network (RNN)
• Reinforcement Learning from Human Feedback (RLHF)
• Regularization
• RoBERTa

S

• Semantic Analysis
• Sequence-to-Sequence Model
• State-of-the-Art (SOTA)
• Supervised Learning
• Sentiment Analysis
• Sequence Modeling

T

• Token
• Tokenization
• Transformer Model
• Transfer Learning
• Turing Test
• Text Generation
• Token Classification
• Training Data

U

• Unsupervised Learning
• Unstructured Data
• Underfitting

V

• Vocabulary Size
• Validation Set
• VAE (Variational Autoencoder)

W

• Weight Initialization
• Word Embedding
• Word2Vec

X

• XAI (Explainable AI)
• Xavier Initialization
• XLNet

Y

• Yield (in terms of output)

Z

• Zero-shot Learning