The Predictive Edge

CHAPTER 4Advanced Topics in LLMs

This chapter explores advanced topics in LLMs, and you need a solid technical background or to be interested in the deeper mechanics of training or fine‐tuning these models for specialized tasks. It is designed as a comprehensive guide to understanding LLMs, whether for academic research, industry application, or personal project enhancement. You can safely go to the next chapter for now and return for reference when you need information related to an advanced project. The advanced LLM techniques covered here will become relevant when you are looking to squeeze out every drop of performance for demanding applications in investing.

Architectures Powering LLMs

LLMs stand at the forefront of AI's innovative edge, driving progress in fields as diverse as natural language processing, content generation, and semantic analysis. The efficacy and versatility of an LLM are significantly determined by its underlying architecture. This section unpacks the complex structures that power these sophisticated models, offering insights into their design, functionality, and the various approaches adopted in their development. By exploring these architectures' distinctive features, strengths, and limitations, you will understand what makes LLMs capable of their impressive feats and how to leverage these insights for specific computational tasks.

The architecture of an LLM plays a critical role in determining its performance for various tasks. Each architecture ...