Summary
In this chapter, we looked at several exciting emerging areas in LLM research including improvements in the generation of diverse responses, advances in reinforcement learning that can improve performance on human-aligned tasks, and methods of training that allow complex models to be distilled into simpler ones through algebraic optimization or student-teacher model designs. Furthermore, in the domain of LLM usage, we looked at ways that predictive inaccuracy through hallucination can be mitigated through improvements in model training and inference. We also examined advances in multi-modal and multi-agent models that allow multiple data types and models to coordinate on sophisticated problems.
If you are interested in exploring these topics in more detail, the References section contains links to more in-depth resources on each of these topics.
In the next chapter, we’ll turn to models for image generation using Variational Autoencoders (VAEs), which involve...
