Understanding Large Language Models

A series of vocabulary flashcards summarizing key concepts from the lecture on large language models (LLMs) and their underlying technologies.

Large Language Models (LLMs)

Deep neural network models developed for natural language processing, capable of understanding and generating human-like text.

Transformer Architecture

A deep learning architecture that allows models to pay selective attention to different parts of the input, crucial for LLMs.

Next-Word Prediction

A training task in which models predict the next word in a sequence, forming a foundation for language models.

Self-Supervised Learning

A learning mechanism in which a model generates its own labels from the input data instead of requiring labeled datasets.

Generative AI (GenAI)

A form of artificial intelligence that encompasses models capable of generating new content, including text, images, and media.

Fine-Tuning

The process of training a pretrained LLM on a smaller, labeled dataset for specific tasks, improving its performance.

Emergent Behavior

Capabilities that arise in models, allowing them to perform tasks not explicitly taught during training, such as translation.

Pretraining

The initial phase of training an LLM on a large, diverse dataset to develop a broad understanding of language.

BERT

A bidirectional transformer model that specializes in masked word prediction and is used primarily for text classification tasks.

GPT (Generative Pretrained Transformer)

A type of LLM focused on generating text by predicting the next word in a sequence, widely used for text completion tasks.

Deep Learning

A subset of machine learning that uses multilayer neural networks to model complex patterns in data.

Natural Language Processing (NLP)

A field of study focused on the interaction between computers and human language, encompassing tasks like translation and sentiment analysis.

Machine Translation

The use of LLMs to automatically translate text from one language to another.

Text Summarization

The process of condensing text into a shorter version while preserving its main ideas, achievable through LLMs.

Attention Mechanism

A component of transformer architecture that allows the model to focus on different parts of the input text when generating output.

Parameters

The adjustable weights in a neural network that are optimized during training to enhance the model's predictions.