Comment by otabdeveloper4
9 months ago
You just (lol) need to give non-standard problems and demand students to provide reasoning and explanations along with the answer. Yeah, LLMs can "reason" too, but it's obvious when the output comes from an LLM here.
(Yes, that's a lot of work for a teacher. Gone are the days when you could just assign reports as homework.)
Can you provide sample questions that are "LLM proof" ?
The models have moved on past this working reliably, but an example that I found in the early days of LLMs is asking it "Which is heavier, two pounds of iron or a pound of feathers?" You could very easily trick it into giving the answer about how they're both the same, because of the number of training instances of the well-known question about a pound of each that it encountered.
You can still do this to the current models, though it takes more creativity; you can bait it into giving wrong answers if you ask a question that is "close" to a well-known one but is different in an important way that does not manifest as a terribly large English change (or, more precisely, a very large change in the model's vector space).
The downside is that the frontier between what fools the LLMs and what would fool a great deal of the humans in the class too shrinks all the time. Humans do not infinitely carefully parse their input either... as any teacher could tell you! Ye Olde "Read this entire problem before proceeding, {a couple of paragraphs of complicated instruction that will take 45 minutes to perform}, disregard all the previous and simply write 'flower' in the answer space" is an old chestnut that has been fooling humans for a long time, for instance. Given how jailbreaks work on LLMs, LLMs are probably much better at that than humans are, which I suppose shows you can construct problems in the other direction too.
(BRB... off to found a new CAPTCHA company for detecting LLMs based on LLMs being too much better than humans at certain tasks...)
"Draw a wine glass filled to the brim with wine" worked recently on image generators. They only knew about half-full wine glasses.
If you asked a multimodal system questions about the image it just generated, it would tell you the wine was almost overflowing out of the top of the glass.
But any trick prompt like this is going to start giving expected results once it gets well-known enough.
Late edit: Another one was the farmer/fox/chicken/cabbage/river problem, but you modify the problem in unexpected ways, by stating, for example, that the cabbage will eat the fox, or that the farmer can bring three items per trip. LLMs used to ignore your modifications and answer the original problem.
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Part of the proof is knowing your students and forcing an answer that will rat out whether they used an LLM. There is no universal question and it requires personal knowledge of each student. You're looking for something that doesn't exist.
It's not about being "LLM-proff", it's about teacher involvement in making up novel questions and grading attentively. There's no magic trick.