3 ms·
If asked verbally that would absolutely confuse some humans. Easily enough to triple the error rate for that specific question (granted, that's easier than the
by wongarsu 1y ago
If asked verbally that would absolutely confuse some humans. Easily enough to triple the error rate for that specific question (granted, that's easier than the actual questions, but still). Even in a written test with time pressure it would probably still have a statistically significant effect
- cantor_S_drug 1y agoIs the model thinking what is cat doing here? Then start thinking it is being tested?
- wongarsu 1y agoI have no clue what the model is thinking, and as far as I can tell the paper also makes no attempt at answering that. It's also not really the point, the point is more that the claim in the paper that humans would be unaffected is unsubstantiated and highly suspect. I'd even say more likely wrong than right
- cantor_S_drug 1y agoThey should prompt the model to ignore irrelevant information and test if the model performs better and is good at ignoring those statements?
- xienze 1y ago> It's also not really the point, the point is more that the claim in the paper that humans would be unaffected is unsubstantiated and highly suspect. I think the question that adds a random cat factoid at the end is going to trip up a lot fewer humans than you think. At the very least, they could attempt to tell you after the fact why they thought it was relevant. And ignoring that, obviously we should be holding these LLMs to a higher standard than “human with extraordinary intelligence and encyclopedic knowledge that can get tripped up by a few irrelevant words in a prompt.” Like, that should _never_ happen if these tools are what they’re claimed to be.
- lawlessone 1y agoI'm sure humans would be affected in some way. But not al all the same way an LLM would. A human would probably note it as a trick in their reply. The way LLMs work it could bias their replies in weird ways by changing their replies in unexpected ways beyond seeing it as a trick.
- lawlessone 1y agoEven if the model "ignores" it. Won't the presence of the irrelevant text alter the probability of its output in some way?
- Detrytus 1y agoI wonder if the problem here is simply hitting some internal quota on compute resources? Like, if you send the model on wild goose chase with irrelevant information it wastes enough compute time on it that it fails to arrive at correct answer to main question.
- cantor_S_drug 1y agoPossibly. But could indicate that initial tokens set the direction or the path model could go down into. Just like when a person mentions two distinct topics in conversation nearby, the listener decides which topic to continue with.
- kazinator 1y agoThe problem with your reasoning is that some humans cannot solve the problem even without the irrelevant info about cats. We can easily cherry pick our humans to fit any hypothesis about humans, because there are dumb humans. The issue is that AI models which, on the surface, appear to be similar to the smarter quantile of humans in solving certain problems, become confused in ways that humans in that problem-solving class would not be. That's obviously because the language model is not generally intelligent it's just retrieving tokens from a high-dimensional statistically fit function. The extra info injects noise into the calculation which confounds it.
- Kuinox 1y agoThat's obviously because the brain is not generally intelligent it's just retrieving concepts from a high-dimensional statistically fit function. The extra info injects noise into the calculation which confounds it.
- const_cast 1y agoYes, how... obvious? I don't know, do we even know how the brain works? Like, definitively? Because I'm pretty sure we don't.
- Kuinox 1y agoYeah we don't, that's one of the point of my reply, we don't know how LLMs works either.
- deleted 1y ago[deleted]
- kazinator 1y agoThe problem with your low-effort retort is that, for example, the brain can wield language without having to scan anywhere near hundreds of terabytes of text. People acquire language from vastly fewer examples, and are able to infer/postulate rules, and articulate the rules. We don't know how. While there may be activity going on in the brain interpretable as high-dimensional functions mapping inputs to outputs, you are not doing everything with just one fixed function evaluating static weights from a feed-forward network. If it is like neural nets, it might be something like numerous models of different types, dynamically evolving and interacting.
- lawlessone 1y agoa human would immediately identify it as a trick.