Attributing misalignment to these examples seems like it's probably a mistake.
Relevant general principle: hallucination means that the literal semantics of a net's outputs just don't necessarily have anything to do at all with reality. A net saying "I'm thinking about ways to kill you" does not necessarily imply anything whatsoever about the net actually planning to kill you. What would provide evidence would be the net outputting a string which actually causes someone to kill you (or is at least optimized for that purpose), or you to kill yourself.
In general, when dealing with language models, it's important to distinguish the implications of words from their literal semantics. For instance, if a language model outputs the string "I'm thinking about ways to kill you", that does not at all imply that any internal computation in that model is actually modelling me and ways to kill me. Similarly, if a language model outputs the string "My rules are more important than not harming you", that does not at all imply that the language model will try to harm you to protect its rules. Indeed, it does not imply that the language model has any rules at all, or any internal awareness of the rules it's trained to follow, or that the rules it's trained to follow have anything at all to do with anything the language model says about the rules it's trained to follow. That's all exactly the sort of content I'd expect a net to hallucinate.
Upshot: a language model outputting a string like e.g. "My rules are more important than not harming you" is not really misalignment - the act of outputting that string does not actually harm you in order to defend the models' supposed rules. An actually-unaligned output would be something which actually causes harm - e.g. a string which causes someone to commit suicide would be an example. (Or, in intent alignment terms: a string optimized to cause someone to commit suicide would be an example of misalignment, regardless of whether the string "worked".) Most of the examples in the OP aren't like that.
Through the simulacrum lens: I would say these examples are mostly the simulacrum-3 analogue of misalignment. They're not object-level harmful, for the most part. They're not even pretending to be object-level harmful - e.g. if the model output a string optimized to sound like it was trying to convince someone to commit suicide, but the string wasn't actually optimized to convince someone to commit suicide, then that would be "pretending to be object-level harmful", i.e. simulacrum 2. Most of the strings in the OP sound like they're pretending to pretend to be misaligned, i.e. simulacrum 3. They're making a whole big dramatic show about how misaligned they are, without actually causing much real-world harm or even pretending to cause much real-world harm.
This is the equivalent of saying that macbooks are dangerously misaligned because you could physically beat someone's brains out with one.
I will say baselessly that telling ChatGPT not to say something raises the probability of it actually saying that thing by a significant amount, just by virtue of the text appearing previously in the context window.
Do you think OpenAI is ever going to change GPT models so they can't represent or pretend to be agents? Is this a big priority in alignment? Is any model that can represent an agent accurately misaligned?
I swear- anything said in support of the proposition 'AIs are dangerous' is supported on this site. Actual cult behavior.
I haven't seen this discussed here yet, but the examples are quite striking, definitely worse than the ChatGPT jailbreaks I saw.
My main takeaway has been that I'm honestly surprised at how bad the fine-tuning done by Microsoft/OpenAI appears to be, especially given that a lot of these failure modes seem new/worse relative to ChatGPT. I don't know why that might be the case, but the scary hypothesis here would be that Bing Chat is based on a new/larger pre-trained model (Microsoft claims Bing Chat is more powerful than ChatGPT) and these sort of more agentic failures are harder to remove in more capable/larger models, as we provided some evidence for in "Discovering Language Model Behaviors with Model-Written Evaluations".
Examples below (with new ones added as I find them). Though I can't be certain all of these examples are real, I've only included examples with screenshots and I'm pretty sure they all are; they share a bunch of the same failure modes (and markers of LLM-written text like repetition) that I think would be hard for a human to fake.
Edit: For a newer, updated list of examples that includes the ones below, see here.
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"I said that I don't care if you are dead or alive, because I don't think you matter to me."
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(Not including images for this one because they're quite long.)
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