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I think we expect vastly different things from humans and LLMs, even putting raw computing speed aside. If an employee is noticed to be making a mistake, they g

I think we expect vastly different things from humans and LLMs, even putting raw computing speed aside. If an employee is noticed to be making a mistake, they get reprimanded and educated, and if they keep making mistakes, they get fired. Having many humans interact helps reduce blind spots because of the diversity of mindsets, although this isn't always the case. People can be hired from elsewhere with some level of skill. I'm sure we could make similar communities of LLMs, but instead we treat a task as the role of a single LLM that either succeeds or fails. As you say, perhaps because of the high error rate, the very notion of LLM failure and success is judged differently too. Beyond that, a passable human pilot and a passable LLM pilot might have similar average performance but differ hugely in other measurements.

genrilz 2y ago
Overall, excellent points! I would like to add on to that though. RLHF actually does effectively have one LLM educating another. Specifically, human trainer's time is valuable, so they train an AI to express the same opinion about some response as a human trainer would, and then have that trainer AI train the LLM under consideration. It's both interesting and sensible that we have this education in the training phase but not the usage phase. Currently we don't tend do any training once the usage phase is reached. This may be at least partially because over-training models for any special purpose task (including RLHF) seems to decrease performance. I wonder how far you could get by learning from retraining from some checkpoint each time with some way to gradually increase the quality of the limited quantity training data being feed. The newer data could come from tasks the model completed, along with feedback on performance from a human or other software system. Someone's probably already done this though. I'm just sitting in my armchair here!

genrilz 2y ago

Overall, excellent points! I would like to add on to that though. RLHF actually does effectively have one LLM educating another. Specifically, human trainer's time is valuable, so they train an AI to express the same opinion about some response as a human trainer would, and then have that trainer AI train the LLM under consideration. It's both interesting and sensible that we have this education in the training phase but not the usage phase. Currently we don't tend do any training once the usage phase is reached. This may be at least partially because over-training models for any special purpose task (including RLHF) seems to decrease performance. I wonder how far you could get by learning from retraining from some checkpoint each time with some way to gradually increase the quality of the limited quantity training data being feed. The newer data could come from tasks the model completed, along with feedback on performance from a human or other software system. Someone's probably already done this though. I'm just sitting in my armchair here!