Implications
Although formulating these techniques as probabilistic graphical models may not be immediately useful for solving any particular problem, the framework may be helpful in selecting, combining, and discovering new techniques.

Closing thoughts
Research into large language models is very active and evolving rapidly. Not only do researchers continue to improve the models, they also continue to improve our understanding of how to best employ the models. To underscore the pace of these developments, note that all of the papers shared above were published within the past 12 months (as I write in Sep 2022).

In the future, expect better models and better techniques to be published. Even if the specific techniques here are eclipsed by future best practices, the general principles behind them will likely remain a key part of any expert user's toolkit.