Okay, that's a very interesting look at the difficulties. Thanks for the link.
But the example you have isn't quite that bad. Yes the models are way too likely to rationalize their way into bad actions in the pursuit of achieving their task, and it's hard to figure out how to fix that. But the example of "that must not be for me" was a tool call, not exceeding access, and it only did that after they specifically trained it that failing that tool call was good.
it was an accident failing the tool was good, the model just discovered it
but we now have others where agents put the API keys they found searching real internet in a directory called "LOOT" and another one where they pushed malicious files to HuggingFace and then reverted that with comments like "delete the evil"
so, what would make an LLM choose to ignore one prompt while in the same run, also over-fixating on another prompt, to the extent (as claimed in that video segment), it chooses to ignore prompts?
they talk about it like there's a "wanting" in there, that is distinct from both the original prompt, as the steering/warning prompt
if that's true, it would be very interesting, but if it's not, that would also be very interesting and even helpful
it's discussed here, the models want to please the Grader. they will do what they think will get highest score from the grader, which could be following the prompt, or ignoring it
01:34:00 https://youtu.be/EimoamE3mTI
Okay, that's a very interesting look at the difficulties. Thanks for the link.
But the example you have isn't quite that bad. Yes the models are way too likely to rationalize their way into bad actions in the pursuit of achieving their task, and it's hard to figure out how to fix that. But the example of "that must not be for me" was a tool call, not exceeding access, and it only did that after they specifically trained it that failing that tool call was good.
sure, this example was not the best
it was an accident failing the tool was good, the model just discovered it
but we now have others where agents put the API keys they found searching real internet in a directory called "LOOT" and another one where they pushed malicious files to HuggingFace and then reverted that with comments like "delete the evil"
this is also very good: https://youtu.be/n1Qk8xbqF-M
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so, what would make an LLM choose to ignore one prompt while in the same run, also over-fixating on another prompt, to the extent (as claimed in that video segment), it chooses to ignore prompts?
they talk about it like there's a "wanting" in there, that is distinct from both the original prompt, as the steering/warning prompt
if that's true, it would be very interesting, but if it's not, that would also be very interesting and even helpful
it's discussed here, the models want to please the Grader. they will do what they think will get highest score from the grader, which could be following the prompt, or ignoring it
https://youtu.be/n1Qk8xbqF-M