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

It’s not more going on, it’s that it had such a large training set of data that these false vs true statements are likely covered somewhere in it’s set and the probability states it should assign true or false to the statement.

That’s not how it works at all.

And then look at that your next paragraph states exactly that, the models trained on true false datasets performed extremely well at performing true or false. It’s saying the model is encoding or setting weights to the true and false values when that’s the majority of its data set. That’s basically it, you are reading to much into the paper.

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.

I also recommend reading up on the other papers I mentioned, as this isn’t an isolated finding, but part of a larger trend that’s being found over and over in the past year.

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SmoothIsFast@citizensgaming.com ⁨9⁩ ⁨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 ⁨9⁩ ⁨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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