3 ms·
This is approximately where I wound up. I had an initial knee-jerk reaction after reading the paper, but your kind of reasoning brought me back down to earth.
by bob1029 3y ago
This is approximately where I wound up. I had an initial knee-jerk reaction after reading the paper, but your kind of reasoning brought me back down to earth.
I don't know that I even want these things to generalize. I get it in academic terms, but from a CTO perspective how much this matters to the business? We already know our policies and procedures and aren't exactly excited about the idea of something getting curious with flipping things around.
I don't want it to generalize and start making weird assumptions like "some men are tall". I want it to say "I don't know", or provide a statistical signal indicating the same.
- user_named 3y agoThen you don't want an LLM you want something deterministic
- bob1029 3y agoThere is no opportunity for nuance?
- sgt101 3y agoThere's an opportunity to build a completely different technology. LLM's have a specific architecture that makes them good at approximate retrieval. They have a specific learning process that makes them able to do this over a vast training set. Doing things like reasoning about facts is possible using either symbolic or specific sub symbolic structures, but combining these with an LLM is a bit tricky. It's something that could be done for a demo in a 6hr/day/week project (depends on the demo) but to do it for real in an application... or as a robust bit of science... well... 6 people for 6 mths/years/?