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Can you say that you don't know in enough detail how a transformer (and the whole modern training pipeline) works, thus can't really know whether it knows anything in a meaningful way? Because I'm pretty sure (then again I may be wrong too…) you don't know for certain, yet this doesn't stop you from having a strong opinion. Accurate calibration of confidence is almost as hard as positive knowledge, because, well, unknown unknowns can affect all known bits, including values for known unknowns and their salience. It's a problem for humans and LLMs in comparable measure, and our substrate differences don't shed much light on which party has it inherently harder. Whether LLMs can develop a structure that amounts to meta-knowledge necessary for calibration, and not just perform well due to being trained on relevant data, is not something that can just be intuited from high-level priors like "AI returns the most likely token".
What does it mean to know anything? What distinguishes a model that knows what it knows from one that doesn't? This is a topic of ongoing research. E.g. the Anthropic paper Language Models (Mostly) Know What They Know concludes:
GPT-4, interestingly, is decently calibrated out of the box but then it gets brain-damaged by RLHF. Hlynka, on the other hand, is poorly calibrated, therefore he overestimates his ability to predict whether ChatGPT will hallucinate or reasonably admit ignorance on a given topic.
Also, we can distinguish activations for generic output and for output that the model internally evaluates as bullshit.
John Schulman probably understands Transformers better than either of us, so I defer to him. His idea of their internals, expressed in the recent talk on RL and Truthfulness is basically that that they develop a knowledge graph and a toolset for operations over that graph; this architecture is sufficient to eventually do good at hedging and expressing uncertainty. His proposal to get there is unsurprisingly to use RL in a more precise manner, rewarding correct answers, correct hedges somewhat, harshly punishing errors, and giving 0 reward for admission of ignorance.
I suppose we'll see how it goes.
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