Large Language Models

Large Language Models (LLMs) are a class of foundational AI models trained on vast datasets, enabling them to understand and generate natural language, among other types of content, to perform diverse tasks. These models have propelled generative AI to the forefront of public interest and are a focal point for organizations adopting artificial intelligence across numerous business functions and use cases.

While LLMs may seem like a sudden innovation, many companies, including IBM, have long been leveraging them to enhance their natural language understanding (NLU) and natural language processing (NLP) capabilities. This evolution has paralleled advances in machine learning, algorithms, neural networks, and the transformer models that underpin these sophisticated AI systems.

Unlike domain-specific models, which are costly and complex to build and maintain, LLMs offer broad utility across multiple applications. They deliver superior performance by harnessing synergies from extensive training data and infrastructure investments.

LLMs have revolutionized NLP and AI, with notable examples including OpenAI’s GPT-3 and GPT-4, supported by Microsoft; Meta’s Llama models; and Google’s BERT/RoBERTa and PaLM models. IBM’s recent Granite model series on watsonx.ai has become the generative AI backbone for products like watsonx Assistant and watsonx Orchestrate.