@fasterandworse@dgerard I mean, it is absurd. But it is how it works: an LLM is a black box from a programming perspective, and you cannot directly control what it will output.
So you resort to pre-weighting certain keywords in the hope that it will nudge the system far enough in your desired direction.
There is no separation between code (what the provider wants it to do) and data (user inputs to operate on) in this application 🥴
That’s the standard response from last decade. However, we now have a theory of soft prompting: start with a textual prompt, embed it, and then optimize the embedding with a round of fine-tuning. It would be obvious if OpenAI were using this technique, because we would only recover similar texts instead of verbatim texts when leaking the prompt (unless at zero temperature, perhaps.) This is a good example of how OpenAI’s offerings are behind the state of the art.
@fasterandworse @dgerard I mean, it is absurd. But it is how it works: an LLM is a black box from a programming perspective, and you cannot directly control what it will output.
So you resort to pre-weighting certain keywords in the hope that it will nudge the system far enough in your desired direction.
There is no separation between code (what the provider wants it to do) and data (user inputs to operate on) in this application 🥴
That’s the standard response from last decade. However, we now have a theory of soft prompting: start with a textual prompt, embed it, and then optimize the embedding with a round of fine-tuning. It would be obvious if OpenAI were using this technique, because we would only recover similar texts instead of verbatim texts when leaking the prompt (unless at zero temperature, perhaps.) This is a good example of how OpenAI’s offerings are behind the state of the art.