A Beginner’s Guide to Large Language Models

Large language models (LLMs) are deep learning algorithms that can recognize, summarize, translate, predict, and generate content using very large datasets.

Large language models largely represent a class of deep learning architectures called transformer networks. A transformer model is a neural network that learns context and meaning by tracking relationships in sequential data, like the words in this sentence.

As LLMs have grown in size, so have their capabilities. Broadly, LLM use cases for text-based content can be divided up in the following manner: 

• Generation (e.g., story writing, marketing content creation)
• Summarization (e.g., legal paraphrasing, meeting notes summarization)
• Translation (e.g., between languages, text-to-code)
• Classification (e.g., toxicity classification, sentiment analysis)
• Chatbot (e.g., open-domain Q+A, virtual assistants)

Large language models are still in their early days, and their promise is enormous; a single model with zero-shot learning capabilities can solve nearly every imaginable problem by understanding and generating human-like thoughts instantaneously. The use cases span across every company, every business transaction, and every industry, allowing for immense value-creation opportunities.