humans are your god

LLMs are brittle. They can be taught and thereafter never learn again or they can be continually updated in various ways, all of which lead to forgetting things that are important. one of the ways to prevent what is called "catastrophic forgetting" in LLMs is called Elastic Weight Consolidation. This technique involves constraining the weights being used by the LLM against forgetting. Of course you have a problem here. Constrain too much and it can't learn anything new. Constrain too little and it forgets everything it used to know. What if there was a way to constrain it only along things that are important?

Using primary human input as a proxy for what is important this is possible. This secondary LLM would be trained on the primary LLM's previously held data. It would combine that data with new experiences both llms are receiving. Its output would be a very limited set of concepts that are pertinent to both the previous data and what the human operator is asking for. For example if the human operator input, "I need some water" the secondary LLM would highlight the places those words influence the primary LLM's matrix. These places and only these places would receive a much stronger (decreasing over time) constraint against being forgotten. The primary LLM would now be more "focused" for lack of a better word on the concepts surrounding the human in question and his need for water.