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Comment on Study finds that Chat GPT will cheat when given the opportunity and lie to cover it up later.

SmoothIsFast@citizensgaming.com ⁨11⁩ ⁨months⁩ ago

You have no idea what you are talking about. When they train data they have two sets. One that fine tunes and another that evaluates it. You never have the training data in the evaluation set or vice versa.

That’s not what I said at all, I said as the paper stated the model is encoding trueness into its internal weights during training, this was then demonstrated to be more effective when given data sets with more equal distribution of true and false data points were used during training. If they used one-sided training data the effect was significantly biased. That’s all the paper is describing.

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kromem@lemmy.world ⁨11⁩ ⁨months⁩ ago

I said as the paper stated the model is encoding trueness into its internal weights during training

So how is this not what I originally said, that LLMs are capable of abstracting the concepts of truth vs falsehood into linear representations? Which again, is the key point of the paper:

Probes trained on likely have some effect, but it is small and inconsistent. For instance, in the false→true case, intervening along the logistic regression direction of likely has the opposite of the intended effect, so we leave it unreported. This reinforces our case that LLMs represent truth and not only text likelihood. […]

Probes trained on likely have some effect, but it is small and inconsistent. For instance, in the false→true case, intervening along the logistic regression direction of likely has the opposite of the intended effect, so we leave it unreported. This reinforces our case that LLMs represent truth and not only text likelihood.

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