This chapter covers
- Understanding the capabilities of language models
- Selecting suitable language models
- Customizing language models for specific tasks
- Considering language models in the wider application context
- Evaluating language models
In this chapter, we’ll dive into the world of language models (LMs), which can be used for a wide variety of tasks, starting with content creation and moving on to tasks such as text summarization, translation, and more complex problem solving. The chapter will provide you with a solid understanding of LMs to help you make informed decisions about model selection, deployment, customization, and risk management. You also need to support your engineers in making design decisions about the integration, adaptation, and evaluation of LMs within the larger AI system you’re building.
Terminology While giant language models were the main “culprit” of the generative AI boom, there’s also a trend toward downscaling and using smaller, more efficient models. In the following, I use language model (LM) as a general term encompassing both large language models (LLMs) with more than 2 billion (2 B) parameters and small language models (SLMs) with fewer than 2 B parameters.