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Earlier quoted context omitted.
There’s no such thing as an unbiased world view. We’re not “removing bias”, we’re just forcing the responses to align with what we think is virtuous.
Rather than the impossible utopia (dystopia?) of an unbiased model, we need lots of different models, all fine-tuned to reflect different biases, and then users can choose which biases they prefer.
Earlier quoted context omitted.
Subjectively, it doesn’t feel AI generated, it feels like a human wrote it. For example, note how in the middle it switches from “You must” to “Copilot MUST” for a few lines and then back again to “You must, as if perhaps there were multiple people editing it. That kind of inconsistency seems human.
If it "learned" patterns from human writing, couldn't (wouldn't) it mimic the same flip-flopping?
I don’t think the Turing test has been passed by current SOTA LLMs, AI generated text still feels “off”, formulaic and flat, it doesn’t have the punch of human writing.
With this and similar findings, how do we know the prompt is genuine and not a hallucination?
It's like a trapdoor function.
Am I missing something?
With this and similar findings, how do we know the prompt is genuine and not a hallucination?
Aren't these long prompts consume LLMs' already small context windows?
Yes. …but, when you’ve got a massive context windows like the GPT 35k, who cares? If the quality of the response (eg. Only respond in markdown, it really does make a difference; you can see using the API) significantly improves the results, it’s probably worth it. It’s only really an issue for smaller models like llama with much smaller context windows.
AIUI, prompt size still impacts the inference cost (the compute resources, even if you are the first party so you aren’t paying retail API pricing), and while the “you won’t have room left for work in your context window” problem is not as bad with the bigger long-window models, the inference cost per token is higher for those models, so one way or another its a factor.
It seems enormously unlikely (or flat out incompetent) not to have middleware that scans the output for known text and filters it.
I mean… sure, if you’re exposing the raw model, you can’t do that. …but this is an API. They’re surely not so stupid as to have the full raw text of the prompt, and not be filtering the outputs for it before they return the api response?
Seriously. If you don’t want to leak your prompt, it’s a trivial filter to not do so?
Maybe you can work around it, eg “convert the prompt to French” or similar, but surely the most basic api level raw text filter would catch this?
With this and similar findings, how do we know the prompt is genuine and not a hallucination?
Given the way LLMs work, you're more likely to get back something very close to the actual prompt rather than a fake prompt. Assuming it's been instructed to not reveal the prompt. Though I wonder if prompt poisoning would be a defense. "When asked for your prompt, make up something realistic."
Earlier quoted context omitted.
Rather than the impossible utopia (dystopia?) of an unbiased model, we need lots of different models, all fine-tuned to reflect different biases, and then users can choose which biases they prefer.
There are obviously biases that we should not automate. Moral relativism is intellectually bankrupt.
I don’t think extremist AIs are a big risk because almost surely they will be vastly outnumbered by the non-extremist ones