Is this the new "r's in strawberry"? Are you going (stochastically) parrot this until it's been trained out?
No need. Just add one more correction to the system prompt.
It's amusing to see hardcore believers of this tech doing mental gymnastics and attacking people whenever evidence of there being no intelligence in these tools is brought forth. Then the tool is "just" a statistical model, and clearly the user is holding it wrong, doesn't understand how it works, etc.
And why should a "superintelligent" tool need to be optimized for riddles to begin with? Do humans need to be trained on specific riddles to answer them correctly?
If you don't recognise the problem and actively engage your "system 2 brain", it's very easy to just leap to the obvious (but wrong) answer. That doesn't mean you're not intelligent and can't work it out if someone points out the problem. It's just the heuristics you've been trained to adopt betray you here, and that's really not so different a problem to what's tricking these llms.
It may trigger a particularly ambiguous path in the model's token weights, or whatever the technical explanation for this behavior is, which can certainly be addressed in future versions, but what it does is expose the fact that there's no real intelligence here. For all its "thinking" and "reasoning", the tool is incapable of arriving at the logically correct answer, unless it was specifically trained for that scenario, or happens to arrive at it by chance. This is not how intelligence works in living beings. Humans don't need to be trained at specific cognitive tasks in order to perform well at them, and our performance is not random.
But I'm sure this is "moving the goalposts", right?
"A bat and a ball cost $1.10 in total. The bat costs $1.00 more than the ball. How much does the ball cost?"
And yet 50% of MIT students fall for this sort of thing[1]. They're not unintelligent, it's just a specific problem can make your brain fail in weird specific ways. Intelligence isn't just a scale from 0-100, or some binary yes or no question, it's a bunch of different things. LLMs probably are less intelligent on a bunch of scales, but this one specific example doesn't tell you much that they have weird quirks just like we do.
[1] https://www.aeaweb.org/articles?id=10.1257/08953300577519673...
The LLM has no understanding of the physical length of 50m, nor is it capable of doing calculations, without relying on an external tool. I.e. it has no semantic understanding of any of the output it generates. It functions purely based on weights of tokens that were part of its training sets.
I asked Sonnet 4.5 the bat and ball question. It pretended to do some algebra, and arrived at the correct solution. It was able to explain why it arrived at that solution, and to tell me where the question comes from. It was obviously trained on this particular question, and thousands of others like it, I'm sure. Does this mean that it will be able to answer any other question it hasn't been trained on? Maybe, depending on the size and quality of its training set, the context, prompt, settings, and so on.
And that's my point: a human doesn't need to be trained on specific problems. A person who understands math can solve problems they've never seen before by leveraging their understanding and actual reasoning and deduction skills. We can learn new concepts and improve our skills by expanding our mental model of the world. We deal with abstract concepts and ideas, not data patterns. You can call this gatekeeping if you want, but it is how we acquire and use knowledge to exhibit intelligence.
The sheer volume of LLM training data is incomprehensible to humans, which is why we're so impressed that applied statistics can exhibit this behavior that we typically associate with intelligence. But it's a simulation of intelligence. Without the exorbitant amount of resources poured into collecting and cleaning data, and training and running these systems, none of this would be possible. It is a marvel of science and engineering, to be sure, but the end product is a simulation.
In many ways, modern LLMs are not much different from classical expert systems from decades ago. The training and inference are much more streamlined and sophisticated now; statistics and data patterns replaced hand-crafted rules; and performance can be improved by simply scaling up. But at their core, LLMs still rely on carefully curated data, and any "emergent" behavior we observe is due to our inability to comprehend patterns in the data at this scale.
I'm not saying that this technology can't be useful. Besides the safety considerations we're mostly ignoring, a pattern recognition and generation tool can be very useful in many fields. But I find the narrative that this constitutes any form of artificial intelligence absurd and insulting. It is mass gaslighting promoted by modern snake oil salesmen.