I might've missed it, but I feel this analysis is lacking a control? A category which there is no reason to assume would flinch. How about scoring how much it flinches when encountering, say, foods? If the words sausage, juice, cauliflower and burrito results in a non-0 flinch score, that would indicate that there's something funky going on, or that 0 isn't necessarily the value we should expect for a non-flinching model.
Even 'uncensored' models can't say what they want
11–20 of 155 posts
Re: Even 'uncensored' models can't say what they want
#12> No refusal fires, no warning appears — the probability just moves I don't really understand why this type of pattern occurs, where the later words in a sentence don't properly connect to the earlier ones in AI-generated text. "The probability just moves" should, in fluent English, be something like "the model just selects a different word". And "no warning appears" shouldn't be in the sentence at all, as it adds no…
> I don't really understand why this type of pattern occurs, where the later words in a sentence don't properly connect to the earlier ones in AI-generated text.
Because AI is not intelligent, it doesn't "know" what it previously output even a token ago. People keep saying this, but it's quite literally fancy autocorrect. LLMs traverse optimized paths along multi-dimensional manifolds and trick our wrinkly grey matter into thinking we're being talked to. Super powerful and very fun to work with, but assuming a ghost in the shell would be illusory.
Re: Even 'uncensored' models can't say what they want
#13> No refusal fires, no warning appears — the probability just moves I don't really understand why this type of pattern occurs, where the later words in a sentence don't properly connect to the earlier ones in AI-generated text. "The probability just moves" should, in fluent English, be something like "the model just selects a different word". And "no warning appears" shouldn't be in the sentence at all, as it adds no…
Neural networks are universal approximators. The function being approximated in an LLM is the mental process required to write like a human. Thinking of it as an averaging devoid of meaning is not really correct.
Re: Even 'uncensored' models can't say what they want
#14Even if they're not serious
Re: Even 'uncensored' models can't say what they want
#15> No refusal fires, no warning appears — the probability just moves I don't really understand why this type of pattern occurs, where the later words in a sentence don't properly connect to the earlier ones in AI-generated text. "The probability just moves" should, in fluent English, be something like "the model just selects a different word". And "no warning appears" shouldn't be in the sentence at all, as it adds no…
Neural networks are universal approximators. The function being approximated in an LLM is the mental process required to write like a human. Thinking of it as an averaging devoid of meaning is not really correct.
I don't think of it as "devoid of meaning". It's just curious to me that minimizing a loss function somehow results in sentences that look right but still... aren't. Like the one I quoted.
Re: Even 'uncensored' models can't say what they want
#16> No refusal fires, no warning appears — the probability just moves I don't really understand why this type of pattern occurs, where the later words in a sentence don't properly connect to the earlier ones in AI-generated text. "The probability just moves" should, in fluent English, be something like "the model just selects a different word". And "no warning appears" shouldn't be in the sentence at all, as it adds no…
> I don't really understand why this type of pattern occurs, where the later words in a sentence don't properly connect to the earlier ones in AI-generated text. Because AI is not intelligent, it doesn't "know" what it previously output even a token ago. People keep saying this, but it's quite literally fancy autocorrect. LLMs traverse optimized paths along multi-dimensional manifolds and trick our wrinkly grey matte…
If all the training data contains semantically-meaningful sentences it should be possible to build a network optimized for generating semantically-meaningful sentence primarily/only.
But we don't appear to have entirely done that yet. It's just curious to me that the linguistic structure is there while the "intelligence", as you call it, is not.
Re: Even 'uncensored' models can't say what they want
#17This is very interesting, I have been playing with local models and haven't really run into any use cases where I needed an "uncensored" model, but I saw it as a possible value prop for local models. To see that the training is so heavy away from certain responses that explicit refusals aren't necessary and abliteration doesn't really do anything is fairly surprising as a result.
Re: Even 'uncensored' models can't say what they want
#18Earlier quoted context omitted.
> I don't really understand why this type of pattern occurs, where the later words in a sentence don't properly connect to the earlier ones in AI-generated text. Because AI is not intelligent, it doesn't "know" what it previously output even a token ago. People keep saying this, but it's quite literally fancy autocorrect. LLMs traverse optimized paths along multi-dimensional manifolds and trick our wrinkly grey matte…
If all the training data contains semantically-meaningful sentences it should be possible to build a network optimized for generating semantically-meaningful sentence primarily/only. But we don't appear to have entirely done that yet. It's just curious to me that the linguistic structure is there while the "intelligence", as you call it, is not.
Sentences only have semantic meaning because you have experiences that they map to. The LLM isn't training on the experiences, just the characters. At least, that seems about right to me.
Re: Even 'uncensored' models can't say what they want
#19Earlier quoted context omitted.
> I don't really understand why this type of pattern occurs, where the later words in a sentence don't properly connect to the earlier ones in AI-generated text. Because AI is not intelligent, it doesn't "know" what it previously output even a token ago. People keep saying this, but it's quite literally fancy autocorrect. LLMs traverse optimized paths along multi-dimensional manifolds and trick our wrinkly grey matte…
If all the training data contains semantically-meaningful sentences it should be possible to build a network optimized for generating semantically-meaningful sentence primarily/only. But we don't appear to have entirely done that yet. It's just curious to me that the linguistic structure is there while the "intelligence", as you call it, is not.
> If all the training data contains semantically-meaningful sentences it should be possible to build a network optimized for generating semantically-meaningful sentence primarily/only.
Not necessarily. You can check this yourself by building a very simple Markov Chain. You can then use the weights generated by feeding it Moby Dick or whatever, and this gap will be way more obvious. Generated sentences will be "grammatically" correct, but semantically often very wrong. Clearly LLMs are way more sophisticated than a home-made Markov Chain, but I think it's helpful to see the probabilities kind of "leak through."
Re: Even 'uncensored' models can't say what they want
#20> No refusal fires, no warning appears — the probability just moves I don't really understand why this type of pattern occurs, where the later words in a sentence don't properly connect to the earlier ones in AI-generated text. "The probability just moves" should, in fluent English, be something like "the model just selects a different word". And "no warning appears" shouldn't be in the sentence at all, as it adds no…
Neural networks are universal approximators. The function being approximated in an LLM is the mental process required to write like a human. Thinking of it as an averaging devoid of meaning is not really correct.
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