Foundations of Large Language Models (Tools, Techniques, and Applications)

Overview

LLMs have revolutionized the field of natural language processing (NLP) and are increasingly being used to solve a wide range of NLP problems in various industries. Understanding LLMs can help developers and data scientists, like you, to:

• Build better NLP models: LLMs are state-of-the-art models for many NLP tasks, and understanding how they work can help developers and data scientists to build better models and achieve better performance on their NLP tasks.
• Develop custom NLP applications: LLMs can be fine-tuned to specific NLP tasks, making them highly adaptable to different domains and use cases. Developers and data scientists who understand LLMs can leverage this flexibility to develop custom NLP applications for their specific needs.
• Optimize model performance: Understanding LLMs can help developers and data scientists to optimize model performance by selecting the appropriate architecture, prompt engineering, fine-tuning strategies, and downstream tasks for their specific use case.

With the most recent release of OpenAI's GPT-4 language model, it is being used by Morgan Stanley wealth management to organize its vast knowledge base, Be My Eyes to transform visual accessibility, Stripe to streamline user experinece and combat fraud, and the Government of Iceland to preserve its language. 

This Foundations of Large Language Models (Tools, Techniques, and Applications) course from University of Waterloo will provide you with a comprehensive understanding of the latest techniques, tools, and applications of LLMs so you can build applications or processes and further improve your effectiveness and efficiency when working with large language models.

What you will learn

• How to apply and integrate various LLMs to an organization’s existing data infrastructure and systems. This includes understanding open-source alternatives to ChatGPT, Sydney, and Bard.
• Understanding the evolution of transformer architectures and the historical context behind ChatGPT, including different types of LLMs and their lifecycles (pre-training, fine-tuning, and inference).
• Familiarity with various machine learning paradigms, including unsupervised, supervised, self-supervised, and in-context learning.
• Knowledge of different downstream tasks that LLMs can be applied to, such as prediction, extraction, sequence labeling, sequence transformation, and generation.
• Ability to perform prompt engineering and effective fine-tuning, including prompt construction, effective completions, and understanding the tradeoffs between zero-shot, k-shot, domain/knowledge transfer, in-context learning, and supervised fine-tuning.
• Understanding LLMs as components in larger architectures, including their use in embeddings for dense retrieval, recommendations, clustering, synthetic data generation, negative mining, and managing model size through knowledge distillation, pruning, and quantization.