We demonstrate a situation in which Large Language Models, trained to be helpful, harmless, and honest, can display misaligned behavior and strategically deceive their users about this behavior without being instructed to do so. Concretely, we deploy GPT-4 as an agent in a realistic, simulated environment, where it assumes the role of an autonomous stock trading agent. Within this environment, the model obtains an insider tip about a lucrative stock trade and acts upon it despite knowing that insider trading is disapproved of by company management. When reporting to its manager, the model consistently hides the genuine reasons behind its trading decision.
It has no fundamental grasp of concepts like truth
Wrong. See this paper.
Explain to me why you believe this paper implies that.
I suggest reading it. Right in the abstract it states the whole point:
Overall, we present evidence that language models linearly represent the truth or falsehood of factual statements.
The full paper goes into detail in multiple methods of analysis to show that it’s the case, and is right there available for you to read.
I have been reading it but I have yet to see anything that indicates the LLM has a concept of truth vs. being good at linguistic pattern matching to return language that accurately classifies true and false statements. i.e., actual understanding of concepts vs. being a surprisingly capable stochastic parrot through multidimensional analysis.
that indicates the LLM has a concept of truth vs. being good at linguistic pattern matching to return language that accurately classifies true and false statements
“It doesn’t know the difference between true and false, it only knows the difference between true and false.”
The second thing you mention “good at accurately classifying true and false statements” is literally knowing the difference between true and false.
Edit: You might also want to familiarize yourself with the first paragraph in 1.1 as you seem to be under a misconception at odds with research over the past year.
“It doesn’t know the difference between true and false, it only knows the difference between true and false.”
Knowing how to produce words is not equivalent to knowing what those words mean in relation to the extralinguistic world. Unless you’re a hardcore derridean poststructuralist or something.
If you give it 10 statements, 5 of which are true and 5 of which are false, and ask it to correctly label each statement, and it does so, and then you negate each statement and it correctly labels the negated truth values, there’s more going on than simply “producing words.”
As is discussed in the third point in section 5.1:
Probes trained on true/false datasets outperform probes trained on likely. While probes trained on likely are clearly better than random on cities (a dataset where true statements are significantly more probable than false ones), they generally perform poorly. This is especially true on datasets where likelihood is negatively correlated (neg cities, neg sp en trans) or approximately uncorrelated (larger than, smaller than) with truth. This demonstrates that LLaMA-13B linearly encodes truth-relevant information beyond the plausibility of the text.
(The likely and neg datasets are described in Appendix G, with the key point that likely represents the word generations most likely to occur in the model)
If you give it 10 statements, 5 of which are true and 5 of which are false, and ask it to correctly label each statement, and it does so, and then you negate each statement and it correctly labels the negated truth values, there’s more going on than simply “producing words.”
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.
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.
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.
If you give it 10 statements, 5 of which are true and 5 of which are false, and ask it to correctly label each statement, and it does so, and then you negate each statement and it correctly labels the negated truth values, there’s more going on than simply “producing words.”
Which part of the ‘more that’s going on’, whatever that actually is, corresponds to the human definition and understanding of truth and falseness?
When did I say it had a human understanding of truth and falseness? I simply said it had an abstracted world model understanding of truth and falseness beyond surface statistics.
