171–180 of 628 posts
Why would asking an LLM to leak its prompt actually cause it to leak its real prompt? You could ask it to leak its weights too and it would probably give you something back but no one would take that seriously. I just don't see architecturally why you should trust that a "leaked prompt" is anything but some statistically generated text, just like everything else an LLM produces.
Earlier quoted context omitted.
Instruction tuning is distinct from RLHF. Instruction tuning teaches the model to understand and respond (in a sensible way) to instructions, versus 'just' completing text. RLHF trains a model to adjust it's output based on a reward model. The reward model is trained from human feedback. You can have an instruction tuned model with no RLHF, RLHF with no instruction tuning, or instruction tuning and RLHF. Totally orth…
In this case Open AI used RLHF to instruct-tune gpt3. Your pedantism here is unnecessary.
Nearly all popular local models are instruction tuned, but are not RLHF'd. The OAI GPT series are not the only LLMs in the world.
Here's why I don't think this leaked prompt is hallucinated (quoting from my tweets https://twitter.com/simonw/status/1657227047285166080 ): Any time something like this happens a bunch of people suspect that it might be a hallucination, not the real prompt I used to think that but I don't any more: prompt leaks are so easy to pull off, and I've not yet seen a documented case of a hallucinated but realistic leak One…
You're committing the toupee fallacy[0]. You think you'd know the fake one if you saw it, and these are real because, well look at them, they must be real. > prompt leaks are so easy to pull off Only if you assume the prompt you're seeing is real. How do you know? [0] https://en.wiktionary.org/wiki/toupee_fallacy
A frustrating thing about LLMs is that often "intuition" is the best we get! And it's really hard to explain intuition to other people.
Something that I find weird about these chat prompts (assuming they are real, not hallucinated): They're almost always written in second person*. "You are an AI programming assistant" "You are about to immerse yourself into the role of another Al model known as DAN" Who are these prompts addressed to? Who does the GPT think wrote them? The thing that confuses me is that these are text token prediction algorithms, und…
Something that I find weird about these chat prompts (assuming they are real, not hallucinated): They're almost always written in second person*. "You are an AI programming assistant" "You are about to immerse yourself into the role of another Al model known as DAN" Who are these prompts addressed to? Who does the GPT think wrote them? The thing that confuses me is that these are text token prediction algorithms, und…
Earlier quoted context omitted.
If you play with a "raw" model such as LLaMA you'll find what you suggest is true. These models do what you'd expect of a model that was trained to predict the next token. It's quite tricky to convince such a model to do what you want. You have to conceptualize it and then imagine an optimal prefix leading to the sort of output you've conceptualized. That said, people discovered some fairly general-purpose prefixes,…
Right. But who's the 'you' who's being addressed by the {:system} prompt? Who is the {:assistant} supposed to think the {:system} is? Why should the {:assistant} output tokens that make it do what the {:system} tells it to? After all, the {:user} doesn't. The {:system} doesn't provide any instructions for how the {:user} is supposed to behave, the {:user} tokens are chosen arbitrarily and don't match the probabilitie…
There isn't much research on what's actually going on here, mainly because nobody has access to the weights of the really good models.
(via https://news.ycombinator.com/item?id=35924293, but we merged that thread hither)
I think that a lot of the limits placed on these models / chat services don't do much to remove underlying bias but rather attempt to obfuscate them from the general public. ChatGPT, Dall-e, etc all make assumptions about identity or politics but try to sidestep direct requests around those topics to appear more neutral... but the bias still exists in the model and affects the answers.
> ChatGPT, Dall-e, etc all make assumptions about identity or politics but try to sidestep direct requests around those topics to appear more neutral... but the bias still exists in the model and affects the answers. In the case of ChatGPT, I’d love to know how much of the bias is in the original (pre)training data, and how much is due to OpenAI’s human trainers. It is so careful to avoid every bias which is condemne…
I'd bet it's almost all in the human trainers, because LLaMA 65B, the closest uncensored alternative, doesn't have the same US Silicon Valley Democrat biases as ChatGPT.
Something that I find weird about these chat prompts (assuming they are real, not hallucinated): They're almost always written in second person*. "You are an AI programming assistant" "You are about to immerse yourself into the role of another Al model known as DAN" Who are these prompts addressed to? Who does the GPT think wrote them? The thing that confuses me is that these are text token prediction algorithms, und…
The prompters don't tell the LLM stories because they think "someone" is in there, but because they need to write the LLM into the place it can help them from before the "predict the next token" part is terribly useful.
Huh, this is the least interesting thing I've written about prompt injection in the last few weeks, but the only one to make it to the Hacker News homepage. Better recent posts: - Delimiters won’t save you from prompt injection - https://simonwillison.net/2023/May/11/delimiters-wont-save-y... - talks about why telling a model to follow delimiters like ``` won't protect against prompt injection, despite that being men…