Comment by HarHarVeryFunny
12 hours ago
Regarding cats on mats ...
If you ask a human to complete the phrase "the cat sat on the", they will probably answer "mat". This is memorization, not understanding. The LLM can do this too.
If you just input "the cat sat on the" to an LLM, it will also likely just answer "mat" since this is what LLMs do - they are next-word input continuers.
If you said "the sat sat on the" to a human, they would probably respond "huh?" or "who the hell knows!", since the human understands that cats are fickle creatures and that partial sentences are not the conversational norm.
If you ask an LLM to explain it's understanding of cats, it will happily reply, but the output will not be it's own understanding of cats - it will be parroting some human opinion(s) it got from the training set. It has no first hand understanding, only 2nd hand heresay.
>If you said "the sat sat on the" to a human, they would probably respond "huh?" or "who the hell knows!", since the human understands that cats are fickle creatures and that partial sentences are not the conversational norm.
I'm not sure what you're getting at here ? You think LLMs don't similarly answer 'What are you trying to say?'. Sometimes I wonder if the people who propose these gotcha questions ever bother to actually test them on said LLMs.
>If you ask an LLM to explain it's understanding of cats, it will happily reply, but the output will not be it's own understanding of cats - it will be parroting some human opinion(s) it got from the training set. It has no first hand understanding, only 2nd hand heresay.
Again, you're not making the distinction you think you are. Understanding from '2nd hand heresay' is still understanding. The vast majority of what humans learn in school is such.
> Sometimes I wonder if the people who propose these gotcha questions ever bother to actually test them on said LLMs
Since you asked, yes, Claude responds "mat", then asks if I want it to "continue the story".
Of course if you know anything about LLMs you should realize that they are just input continuers, and any conversational skills comes from post training. To an LLM a question is just an input whose human-preferred (as well as statistically most likely) continuation is a corresponding answer.
I'm not sure why you regard this as a "gotcha" question. If you're expressing opinions on LLMs, then table stakes should be to have a basic understanding of LLMs - what they are internally, how they work, and how they are trained, etc. If you find a description of LLMs as input-continuers in the least bit contentious then I'm sorry to say you completely fail to understand them - this is literally what they are trained to do. The only thing they are trained to do.
Claude and GPT both ask for clarification
https://claude.ai/share/3e14f169-c35a-4eda-b933-e352661c92c2
https://chatgpt.com/share/6919021c-9ef0-800e-b127-a6c1aa8d9f...
>Of course if you know anything about LLMs you should realize that they are just input continuers, and any conversational skills comes from post training.
No, they don't. Post-training makes things easier, more accessible and consistent but conversation skills are in pre-trained LLMs just fine. Append a small transcript to the start of the prompt and you would have the same effect.
>I'm not sure why you regard this as a "gotcha" question. If you're expressing opinions on LLMs, then table stakes should be to have a basic understanding of LLMs - what they are internally, how they work, and how they are trained, etc.
You proposed a distinction and explained a situation which would make that distinction falsifiable. And I simply told you LLMs don't respond the way you claim they would. Even when models respond mat (Now I think your original point had a typo?), it is clearly not due to a lack of understanding of what normal sentences are like.
>If you find a description of LLMs as input-continuers in the least bit contentious then I'm sorry to say you completely fail to understand them - this is literally what they are trained to do. The only thing they are trained to do.
They are predictors. If the training data is solely text then the output will be more text, but that need not be the case. Words can go in while Images or actions or audio may come out. In that sense, humans are also 'input continuers'.
2 replies →