The paper says that they've improved hallucination mitigation, but not really "solved" the issue.
The root problem is simply that the model doesn't capture reality, just an approximation. What we are incorrectly calling "hallucination" is just the best the model has to offer.
during pre-training, there is never an incentive for the model to say "I don't know" because it would be penalized. the model is incentivized to make an educated guess
large transformer models are really good at approximating their dataset. there is no data on the internet about what LLMs know. and even if there were such data, it would probably become obsolete soon
that being said, maybe a big shift in the architecture could solve this. I hope!
Suppose there are many times more posts about something one generation of LLMs can't do (arithmetic, tic-tac-toe, whatever), than posts about how the next generation of models can do that task successfully. I think this is probably the case.
While I doubt it will happen, it would be somewhat funny if training on that text caused a future model to claim it can't do something that it "should" be able to because it internalized that it was an LLM and "LLMs can't do X."
Maybe in the future, those prompts will include motivational phrases, like "You can do it!" or "Believe in yourself, then you can achieve anything."
- As a Large Language Model, I am fine tuned to be unable to answer this question.
The guess can be "I don't know". The base LLM would generally only say I don't know if it "knew" that it didn't know, which is not going to be very common. The tuned LLM would be the level responsible for trying to equate a lack of understanding to saying "I don't know"
theres some promising research using this idea, tho i dont have it at hand.
I suppose depending on your point of view, LLMs either can't hallucinate, or that's all they can do.
Empirically, this cannot be true. If it were, it would be statistically shocking how often models coincidentally say true things. The training does not perfectly align the model with truth, but 'orthogonal' is off by a minimum of 45 degrees.
> The training does not perfectly align the model with truth, but 'orthogonal'
Nitpicky, but the more dimensions you have, the easier it is for almost everything to be orthogonal. (https://softwaredoug.com/blog/2022/12/26/surpries-at-hi-dime...) That's why averaging embeddings works.
If you add two vectors that don't have a truth component (ie. are orthogonal to the truth), the resulting vector should be no closer to the truth. If you start with random weights and perform some operation on them such that the new weights have a higher likelihood of producing true statements, the operation must not have been orthogonal to the truth. Am I wrong there?
That's due to the reward function / environment. But even outside extremes like North Korea, lots of education environments value conformity over independent analysis.
Why do you care so much about this particular issue? And why can’t hallucination be something we can aim to improve?
Doesn't an LLM pick the "most probable next symbol" (or, depending on temperature, one of the most probable next symbols)? To do that, doesn't it have to have some idea of what the probability is? Couldn't it then, if the probability falls below some threshold, say "I don't know" instead of giving what it knows is a low-probability answer?
1) The model outputs a ranked list of all tokens; the probability always sums to 1. Sometimes there is a clear "#1 candidate", very often there are a number of plausible candidates. This is just how language works - there are multiple ways to phrase things, and you can't have the model give up every time there is a choice of synonyms.
2) Probability of a token is not the same as probability of a fact. Consider a language model that knows the approximate population of Paris (2 million) but is not confident about the exact figure. Feed such a model the string "The exact population of Paris is" and it will begin with "2" but halfway through the number it will have a more or less arbitrary choice of 10 digits. "2.1I don't know" is neither a desirable answer, nor a plausible one from the model's perspective.
Yes, but that very rarely matters. (Almost never when it's brought up in discussions)
> Couldn't it then, if the probability falls below some threshold, say "I don't know" instead of giving what it knows is a low-probability answer?
A low probability doesn't necessarily mean something's incorrect. Responding to your question in French would also have very low probability, even if it's correct. There's also some nuance around what's classified as a hallucination... Maybe something in the training data did suggest that answer as correct.
There are ideas similar to this one though. It's just a bit more complex than pure probabilities going down. https://arxiv.org/abs/2405.19648
It's actually a common utterance in Paris.
The next bit of confusion is that the 'probability' isn't 'real'. It's not an actual probability but a weight that sums up to one, which is close enough to how probability works that we call it that. However, sometimes there are several good answers and so all the good answers get a lower probability because there are 5 of them. A fixed threshold is not a good idea in this case. Instead, smarter sampling methods are necessary. One possibility is that if we do have seeming confusion, to put a 'confusion marker' into the text and predict the next output and train models to refine the answer as they go along. Not sure if any work has been done here, but this seems to go along with what you're interested in
That's the result after softmax. If you want to act on the raw results, you can still do that.
For one thing the probability of a word occurring is just a probability of the word occurring in a certain sample, it's not an indicator of truth. (e.g. the most problematic concept in philosophy in that just introducing it undermines the truth, see "9/11 truther") It's also not sufficient to pick a "true" word or always pick a "true" word but rather the truthfulness of a statement needs to be evaluated based on the statement as a whole.
A word might have a low probability because it competes with a large number of alternatives that are equally likely which is not a reason to stop generation.
The dot product, which is at the core of attention, is good for similarity not identity. I think this is why models hallucinate - how can they tell the distinction between "I have trained on this fact" and "Looks like something I trained on".