Let’s say you spend an unholy amount of processing time training a 70b. You like history. You want a good LLM for historical info.
By the time you upload it the LLM is outdated. Now what?
If you want it to speak accurately about modern events you’d have to retrain it again. Repeating the process over and over, because time keeps moving on while your LLM does not.
This clearly could become more efficient. Optimally, each subject would probably need to be considered a separate file while the central “brain” of the LLM becomes its own structure.
As it stands, updating the entire LLM is very cost prohibitive and makes no sense if you’re trying to work out specific data points. Why, for example, would you want to update the entire Cantonese dictionary when you just want to fix the list of Alaskan donut shops?
I understand that the tech currently has to treat both the information and the “thinking” behind an LLM as one and the same. It seems more efficient, more effective, to separate the two.
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While I’m not an expert, I understand that, in theory, large language models can process an unlimited amount of context. However, there are practical limitations. If we start by training a base model, for example, one with 70 billion parameters, to excel in reasoning and insight extraction, we could then progress to using bigger models to fine-tune . These bigger models could teach our 70b how to handle context windows ranging from 2 to 10 million tokens, essentially allowing us to store up-to-date information in a document. RAG can come in handy here as well.
You’ve basically described the entire purpose behind Retrieval Augmented Generation.