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Why language models hallucinate

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141–150 of 242 posts

Re: Why language models hallucinate

#141
This is a case of the metric becoming the target. The tools used to evaluate LLM performance are shaping the LLM. First you make your tools then your tools make you.

If we take a formal systems approach, then an LLM is a model of a complex hierarchy of production rules corresponding to the various formal and informal grammatical, logical, and stylistic rules and habits employed by humans to form language that expresses their intelligence. It should not be surprising that simply executing the production rules, or a model thereof, will give rise to sentences that cannot be assigned a meaning. It should also give rise to sentences that we cannot prove or make sense of immediately but we would not want to discard these due to uncertainty. Why? because every once in a while the sentence that would be culled is actually the stroke of brilliance we are looking for, uncertainty be damned. The citation here would be literally nearly every discovery ever made.

When I recall information and use it, when I "think", I don't just produce sentences by the rules, formal and informal, I don't consider at all how often I have seen one word precede another in past, rather as I meandre the landscape of a given context, a thought manifold if you will, I am constantly evaluating whether this is in contradiction with that, if this can be inferred from that via induction or deduction, does this preclude that, etc.. That is the part that is missing from an LLM; The uncanny ability of the human mind to reproduce the entire manifold of concepts as they relate to one another in a mesh from any small piece of the terrain that it might recall, and to verify anew that they all hang together unsupported by one's own biases.

The problem is that just as the scarcity of factual information in the corpus makes it difficult to produce, so is actual reasoning rarefied among human language samples. Most of what appears as reasoning is language games and will to power. The act of reasoning in an unbiased way is so foreign to humans, so painful and arduous, so much like bending over backwards or swimming upstream against a strong current of will to power, that almost nobody does it for long.

Re: Why language models hallucinate

#142

Earlier quoted context omitted.

I agree with everything you said except: > Trying to eliminate cases where a stochastic model the size of an LLM gives “undesirable” or “untrue” responses seems rather odd. Take it back to what it is like you say, this is a predictive model, and the work of any ML scientist is to iterate on the model to try and get perfect accuracy on unseen data. It makes sense to want to tune the models to lower the rate of predict…

But we're getting into the limits of knowledge and what is true/untrue. A stochastic model will be wrong sometimes.

Off course, 100% prediction accuracy cannot be achieved.

I just mean that, if you're an ML scientist team, you don't just go, we got 76% accuracy, let's close shop, mail in your resignation, job over.

From that angle, it's not odd at all that the team just continues working and now see if they can achieve greater than 76%.

Re: Why language models hallucinate

#144

Earlier quoted context omitted.

I would assume most people use native subtitles when it's hard to understand what words the actors said.

No that is not the reason. People watch Netflix to switch their brain off - having the text there helps along with the visual and sound to deliver the content. However, text is inferior to both visual and sound as a delivery mechanism.

Subtitles increase the signal to noise ratio. At least in our house. We have to keep the tv low to not wake the child. A volume of 10 with subtitles is similar to volume at 16 without subtitles.

Re: Why language models hallucinate

#145

Earlier quoted context omitted.

But an LLM is not answering "what is truth?". It's "answering" "what does an answer to the question "what is truth?" look like?". It doesn't need a conceptual understanding of truth - yes, there are far more wrong responses than right ones, but the right ones appear more often in the training data and so the probabilities assigned to the tokens which would make up a "right" one are higher, and thus returned more ofte…

>But an LLM is not answering "what is truth?". It's "answering" "what does an answer to the question "what is truth?" look like?". You don't actually know this right? You said what I'm saying is theoretically possible so you're contradicting what you're saying. >You're anthropomorphizing in using terms like "lying to us" or "know the truth". Yes, it's theoretically possible I suppose that they've secretly obtained so…

I think your assessment of the academic take on AI is wrong. We have a rather thorough understanding of the how/why of the mechanisms of LLMs, even if after training their results sometimes surprise us.

Additionally, there is a very large body of academic research that digs into how LLMs seem to understand concepts and truths and, sure enough, examples of us making point edits to models to change the “facts” that they “know”. My favorite of that corpus, though far from the only or most current/advances research , is the Bau Lab’s work: https://rome.baulab.info/

Re: Why language models hallucinate

#146

I find this rather oddly phrased. LLMs hallucinate because they are language models. They are stochastic models of language. They model language, not truth. If the “truthy” responses are common in their training set for a given prompt, you might be more likely to get something useful as output. Feels like we fell into that idea and said - ok this is useful as an information retrieval tool. And now we use RL to reinfo…

This is directly addressed in the article, which states that language models can be trained to abstain when uncertain, by changing how rewards are set up. Incentives currently encourage guessing rather than being honest about uncertainty. If you disagree, it would be helpful to explain why, rather than just responding to the title alone.

Re: Why language models hallucinate

#147

I find this rather oddly phrased. LLMs hallucinate because they are language models. They are stochastic models of language. They model language, not truth. If the “truthy” responses are common in their training set for a given prompt, you might be more likely to get something useful as output. Feels like we fell into that idea and said - ok this is useful as an information retrieval tool. And now we use RL to reinfo…

> I don’t think that’s how humans work.

Every time this comes up I have to bring up Deutsch. He has the best description of intelligent cognition that I've come across. He takes Popper's "conjecture and criticism" approach to science and argues that this guess-and-check loop applies to all our thinking.

E.g. understanding spoken language has some elements of guessing what might have been said and checking that against the sounds we heard. Visual processing has similar analogies.

LLMs seem to be great at conjecturing stuff, but seem incapable of checking or even knowing they need to check.

Re: Why language models hallucinate

#148

Earlier quoted context omitted.

The word "hallucination" mis-characterizes it. LLMs predict the likely tokens to follow the context. And they can make incorrect predictions. LLMs therefore don't have perfect accuracy of prediction. When their predictions are incorrect, people say they "hallucinate". Nobody questions why predictive weather models aren't perfectly accurate, because it makes sense that a prediction can be wrong. Marketing and hype has…

A weather model is made to predict the weather and used to predict the weather, so there you are right. A language model is made to predict language, but used to generate code or answers to math questions, that is not the same situation as a weather model. The language model is not made to solve math or generate correct code, if you ask it to predict the weather it wont try to predict the weather, it will just predic…

That framing is too narrow. A weather model is trained on physics equations but still relies on patterns in past data to make forecasts. A language model is trained on patterns in human text but that text already encodes mathematics, code, and reasoning. When prompted with a math problem, the model is not doing physics but it is reproducing the learned statistical structure of solutions people have written before. The distinction between “predicting language” and “solving math” is smaller than it seems because the training data couples symbols to meaning. Dismissing its outputs as “just predicting words” misses the fact that word distributions encode information-rich representations of knowledge. That is why large models can in practice generate working code, prove theorems, and reason through problems, even if they do so imperfectly. The right comparison is not that people are misusing them, but that they generalize beyond their design intent because language itself is the medium through which so many other domains are expressed.

Re: Why language models hallucinate

#149

Earlier quoted context omitted.

The reality is, language itself does not capture the entirety of what is really going on. And I'd get argue its the poorest way of expressing - but one that enables transmission through various mediums efficiently on a cost basis. E.g. when I explain a concept, what comes to my mind is not a string of letters and words. There is a mix of imagery and even sounds that I may have acquired from learning about a concept -…

I would assume most people use native subtitles when it's hard to understand what words the actors said.

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Re: Why language models hallucinate

#150

I find this rather oddly phrased. LLMs hallucinate because they are language models. They are stochastic models of language. They model language, not truth. If the “truthy” responses are common in their training set for a given prompt, you might be more likely to get something useful as output. Feels like we fell into that idea and said - ok this is useful as an information retrieval tool. And now we use RL to reinfo…

People also tend not to understand the absurdity of assuming that we can make LLMs stop hallucinating. It would imply not only that truth is absolutely objective, but that it exists on some smooth manifold which language can be mapped to.

That means there would be some high dimensional surface representing "all true things". Any fact could be trivially resolved as "true" or "false" simply by exploring whether or not it was represented on this surface. Where or not "My social security number is 123-45-6789" is true could be determined simply by checking whether or not that statement was mappable to the truth manifold. Likewise you could wander around that truth manifold and start generating output of all true things.

If such a thing existed it would make even the wildest fantasies about AGI seem tame.

edit: To simplify it further, this would imply you could have an 'is_true(statement: string): bool' function for any arbitrary statement in English.

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