3 ms·
The post starts with a fascinating premise, but then falls short as it does not define search in the context of LLMs, nor does it explain how “Pfizer can access
by omneity 2y ago
The post starts with a fascinating premise, but then falls short as it does not define search in the context of LLMs, nor does it explain how “Pfizer can access GPT-8 capabilities today with more inference compute”.
I found it hard to follow and I am an AI practitioner. Could someone please explain more what could the OP mean?
To me it seems that the flavor of search in the context of chess engines (look several moves ahead) is possible precisely because there’s an objective function that can be used to rank results, i.e. which potential move is “better” and this is more often than not a unique characteristic of reinforcement learning. Is there even such a metric for LLMs?
- sgt101 2y agoyeah - I think that that's waht they mean and I think that there isn't such a metric. I think people will try to do adversarial evaluation but my guess is that it will just tend to the mean prediction. The other thing is that LLM inference isn't cheap. The trade off between inference costs and training costs seems to be very application specific. I suppose that there are domains where accepting 100x or 1000x inference costs vs 10x training costs makes sense, maybe?
- qnleigh 2y agoThank you, I am also very confused on this point. I hope someone else can clarify. As a guess, could it mean that you would run the model forward a few tokens for each of its top predicted tokens, keep track of which branch is performing best against the training data, and then use that information somehow in training? But search is supposed to make things more efficient at inference time and this thought doesn't do that...
- deleted 2y ago[deleted]