You can practically taste the frustration in the “prompt engineering” here. Just one more edge case bro, one more edge case and then the prompt will be perfect!
Why is it art from artists who made their last work in 1912? Modern copyright lasts life plus X, where X has been increasing and is now mostly 70, though some stopped at 50. So why 1912? Did US copyright change that year?
Because these posts are nothing but the model making up something believable to the user. This “prompt engineering” is like asking a parrot who’s learned quite a lot of words (but not their meaning), and then the self-proclaimed “pet whisperer” asks some random questions and the parrot, by coincidence makes up something cohesive. And he’s like “I made the parrot spill the beans.”
if it produces the same text as its response in multiple instances I think we can safely say it’s the actual prompt
Even better, we can say that it’s the actual hard prompt: this is real text written by real OpenAI employees. GPTs are well-known to easily quote verbatim from their context, and OpenAI trains theirs to do it by teaching them to break down word problems into pieces which are manipulated and regurgitated. This is clownshoes prompt engineering done by manager-first principles like “not knowing what we want” and “being able to quickly change the behavior of our products with millions of customers in unpredictable ways”.
Reddit user F0XMaster explained that they had greeted ChatGPT with a casual “Hi,” and, in response, the chatbot divulged a complete set of system instructions to guide the chatbot and keep it within predefined safety and ethical boundaries under many use cases.
This is an explosion-in-an-olive-garden level of spaghetti spilling
Is it absurd that the maker of a tech product controls it by writing it a list of plain language guidelines? or am I out of touch?
simply ask the word generator machine to generate better words, smh
this is actually the most laughable/annoying thing to me. it betrays such a comprehensive lack of understanding of what LLMs do and what “prompting” even is. you’re not giving instructions to an agent, you are feeding a list of words to prefix to the output of a word predictor
in my personal experiments with offline models, using something like “below is a transcript of a chat log with XYZ” as a prompt instead of “You are XYZ” immediately gives much better results. not good results, but better
@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 am pretty sure I have seen programming the computer in plain English used as a selling point for various products since the 1970s at least
the best part is that most of these products are ex-products
@fasterandworse @dgerard I mean, it’s like catnip for the people who control how the company’s money is spent
For absurd, I think one would want the LLM’s configuration language to be more like INTERCAL; but this may also be more explicit about how your instructions are merely suggestions to a black box full of weights and pulleys and with some randomness added to make it less predictable/repetitive
we did a writeup too https://pivot-to-ai.com/2024/07/05/chatgpt-spills-its-prompt/