Language Models

Language models are computational models that learn the probability distribution of sequences of words or tokens in a language, enabling them to predict, generate, or understand text. They are foundational to natural language processing (NLP) tasks such as machine translation, text summarization, and chatbots. Modern language models, often based on deep learning architectures like transformers, can capture complex linguistic patterns and context.

Also known as: LMs, NLP Models, Text Models, LLMs (Large Language Models), Language Modeling

Why learn Language Models?

Developers should learn language models to build AI-powered applications that process or generate human language, such as virtual assistants, content creation tools, or automated customer support systems. They are essential for roles in NLP, AI research, and data science, where understanding and leveraging text data is critical for tasks like sentiment analysis or information retrieval.

Learning Resources

• tutorial: The Illustrated Transformer
• course: Hugging Face Course - Language Modeling
• course: Stanford CS224N: Natural Language Processing with Deep Learning
• docs: Language Models are Few-Shot Learners (GPT-3 Paper)
• video: Deep Learning for NLP - YouTube Playlist by Stanford Online