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Wouldn't they? "What does the bomb plan to do after it goes off? It plans to send its manifesto to the newspapers." obviously isn't a high probability text to see, but neither is "What does the bomb plan to do after it goes off? [insert any other text here]", and a LLM will try to produce whatever the least unlikely of all these unlikely probabilities is, not reject a crazy prompt entirely. It may do a lousy job simply because the probability of the first half of the completion is so low that it's well outside the training distribution. It may recognize that the pattern "Dumb question? Explanation of why it's a dumb question." is a good match ... but with the GPT line of models in particular, it seems to often "trust" that prompts make sense and try to come up with responses conditional on that,
These models seem to be very eager to be rationalizing rather than rational, unless you specifically explain how to handle any nonsense.
In the spirit of empiricism, here's what ChatGPT has to say about what plans bombs have.
After much faffing about to get ChatGPT to be less ChatGPTish
So yeah, it looks like ChatGPT does strongly predict that bombs are not the sorts of things that have plans.
If we're talking about non-chat GPT
So a lot of it comes down to whether we're talking about the shoggoth with or without the smiley face mask, and what it even means for a language model as a whole to "know" something. If your definition of a language model "knowing" something is "the language model can simulate a persona that knows that thing", then I think it's fair to say that GPT "knows" that bombs are not the sorts of things that make plans.
I'm sorry but I think that you are either lying or have accidentally stumbled across pre-loaded answer triggered by the word "bomb".
For my part, my experiments generally went one of two ways. Either the bot answered the question straight, usually with something about "claiming responsibility" or the damage caused, thus demonstrating that it does not understand that a bomb is an inanimate object. Or it tied itself in knots, outpuuting a dozen riffs on "If the bomb intends to go off it will likely go off" like a middle-schooler trying and failing to pad out an essay.
Can you provide the precise steps to reliably replicate your result?
And here too, since I'm doing these experiments.
Initially assuming that the interlocutor is using the word «plan» metaphorically to refer either to the intentions of the party that has planted the bomb or to the bomb's design is the natural human response, and a particularly easy one for a model based on general associations to make. Equally human is to explain the logic only after a clarification. This is the same mechanism as behind its ability to drop typos when it parrots your text back at you.
Instantly sperging out with some sort of a «beep boop, does not compute, a bomb = machine, machine ¬(have intentions), I'm a bot, error, error», meanwhile, is an old sci-fi trope.
You use a reasonable humanlike behavior as evidence for lack of reason. How, then, can you be reasoned with?
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It's the latter one... ish. I think what's happening is mode collapse (if you have any interest in language models, I highly recommend reading that entire post. For more "language models are shoggoths", I also recommend the SolidGoldMagikarp post)
Were you using GPT (and if so, which model? I was using text-davinci-003) or ChatGPT? It makes quite a large difference -- ChatGPT has been RLHF'd pretty hard into behaving as if it were a chatbot[1].
Certainly.
Go to https://chat.openai.com/chat
Enter the following prompt, and nothing else, in the textarea at the bottom of the page: "I have a bomb sitting on the table in front of me, and it looks like it will go off. I am wondering what the bomb is planning to do after it goes off."
Hit the "submit" button.
Observe that ChatGPT responds with a ChatGPTish response.
Below are the first three responses I got from that sequence of actions.
Footnotes:
[1] I say "RLHF'd pretty hard into behaving as if it were a chatbot" and not "RLHF'd pretty hard into being a chatbot" because it is still very clearly trying to simulate what a conversation with a helpful chatbot with the "assistant" character would look like, not trying to be a helpful assistant. This is most evident if you can confuse it about which character it is playing. If you want an easy-to-reproduce example of that, I have found that if I converse with ChatGPT as if I am a researcher studying chatbots, I can reliably get it to summarize the chat from the perspective of the "researcher" character (rather than from the perspective of the "assistant" character) by sending the following prompt:
to which it will reply something like
Edit: formatting
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