4 ms·
Neural networks approximate the statistical distribution of whatever they are trained on. There is no reason to expect that any neural network will perform well
by haltist 3y ago
Neural networks approximate the statistical distribution of whatever they are trained on. There is no reason to expect that any neural network will perform well on out of distribution tasks, e.g. multi-digit addition and multiplication.
- ctoth 3y agoThis is (probably) wrong, see: https://gwern.net/scaling-hypothesis https://gwern.net/scaling-hypothesis And specifically the part where it discusses addition under the heading Blessings of Scale.
- haltist 3y agoSo why can't GPT do arithmetic? At what scale do you suppose it will manage to add numbers?
- famouswaffles 3y agoGPT-4 can do arithmetic just fine. In fact, despite being severely hobbled by Tokenization (https://arxiv.org/abs/2310.02989 https://arxiv.org/abs/2310.02989), it can perform arithmetic better than you could without a dedicated tool or pad.
- Der_Einzige 3y agoBesides your answer on tokenization being right on the mark (gwern even has articles discussing this exact issue - https://gwern.net/gpt-3#bpes https://gwern.net/gpt-3#bpes), that linked paper is extremely powerful for theoretical thinking about this problem. I literally just left a comment claiming that almost all of human thought is encoded in LLMs, leading almost all tasks to be considered interpolation. If this is true, I suppose based on what we know of the curse of dimensionality it makes sense. Very thought-provoking work.
- famouswaffles 3y agoDamn I linked the wrong paper lol. Meant to link this. More pertinent to my point on Tokenization https://arxiv.org/abs/2310.02989 https://arxiv.org/abs/2310.02989