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There's a ton of answers already, some bad some good, but the core technical issue is that ChatGPT just doesn't do retrieval. It has memorized precisely some strings, so it will regurgitate them verbatim with high probability, but for the most part it has learned to interpolate in the space of features of the training data. This enables impressive creativity, what looks like perfect command of English, and some not exactly trivial reasoning. This also makes it a terrible lawyer's assistant. It doesn't know these cases, it knows what a case like this would look like, and it's piss poor at saying «I don't know». Teaching it to say that when, and only when it really doesn't is an open problem.
To mitigate the immediate issue of hallucinations, we can finetune models on the problem domain, and we can build retrieval-, search- and generally tool-augmented LLMs. In the last two years there have been tons of increasingly promising ideas for how best to do it, for example this one.
As a heavy ChatGPT user, I don’t want it to ever say "I don’t know". I want it to produce the best answer it’s capable of, and then I’ll sanity check the answer anyway.
Well, I want it to say that. I also want people to say that more often. If it doesn't know truth, I don't need some made-up nonsense instead. Least of all I need authoritative confident nonsense, it actually drives me mad.
ChatGPT unlike a human is not inherently capable of discerning what it does or doesnt know. By filtering out low confidence answers, you’d be trading away something it’s really good at — suggesting ideas for solving hard problems without flinching, for something that it’s not going to do well anyway. Just double-check the answers.
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it all depends on the downside of being fed wrong info
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And that right there is your problem.
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It can't say "I don't know" because it actually doesn't "know" anything. I mean, it could return the string "I don't know" if somebody told it that in such and such situation, this is what it should answer. But it doesn't actually have an idea of what it "knows" or "doesn't know". Fine-tuning just makes real answers more likely, but for making fake answers unlikely you should somehow make all potential fake texts be less probable than "I don't know" - I'm not sure how it is possible to do that, given infinite possible fake texts and not having them in the training set? You could limit it to saying things which are already confirmed by some text saying exactly the same thing - but that I expect would severely limit the usability, basically a search engine already does something like that.
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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