> Always curious when someone will figure out how we can elide most of the data from an LLM (but retain the logical ability). I don't actually need an LLM to have a very big internal knowledge base to be useful, so long as it can invoke a search tool...
replyI think this can be achieved already. Take a base model and train only on source code. In fact, the very early Granite models from IBM were like that though it didn't support reasoning which limited its performance.
You can do it too. I don't know how much it will cost to train on just source code repos. $10K in total? Not sure.
I’m a bit skeptical. Without instructional materials from textbooks, programming language reference manuals and guides, as well as general knowledge about logic and discrete mathematics, I doubt a model could work very well.
replyYes. Model would be limited in its performance, but it would perform well in the limited domain because LLM interpolate from training data. They don't think like humans.
replyWe might think that knowledge from logc and discrete math would spill over to coding. Unfortunately, it doesn't seem to work like that. Even 1T parameter LLM fail on tasks if there are no variants of it in the training data.
I'm skeptical that the "logical ability" is much more then the elided data. Obviously some things get fully memorized and other things don't, but I don't think there's anything like functional circuits.
replyIsn’t this essentially what MoE partially solves with varying levels of accuracy?
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