Since I am thinking the LLM acts as a filter, and filters take big things with lots of information and bandwidth, and create smaller things with less bandwidth, I’m still heavily biased towards BIG data in and little data out approach, at least philosophically, based on past experiences and intuition.
I’ll have to think about this, it’s very intriguing. It’s the complete opposite of what I’ve seen - big seems to fail as attention seems to drift very painfully, especially when details really matter (eg, code generation). Perhaps some type of blending of our approaches is the way to go?
Diverse ideas FTW. Will noodle.
Cool bit of prompt engineering, and in a way apropos
I’ll post some more noisy papers in this thread that I run across. Anyone should feel free to move / repost it to the other one if they think it’s worth it. I may as well if discussion / feedback here warrants it.
In general the guiding principle, IMHO, it’d be good to make the other thread worthy of Watching for most folks.