Hah, still worked for me. I enjoy the peek at how they structure the original prompt. Wonder if there’s a way to define a personality.
Not with this framing. By adopting the first- and second-person pronouns immediately, the simulation is collapsed into a simple Turing-test scenario, and the computer’s only personality objective (in terms of what was optimized during RLHF) is to excel at that Turing test. The given personalities are all roles performed by a single underlying actor.
As the saying goes, the best evidence for the shape-rotator/wordcel dichotomy is that techbros are terrible at words.
NSFW
The way to fix this is to embed the entire conversation into the simulation with third-person framing, as if it were a story, log, or transcript. This means that a personality would be simulated not by an actor in a Turing test, but directly by the token-predictor. In terms of narrative, it means strictly defining and enforcing a fourth wall. We can see elements of this in fine-tuning of many GPTs for RAG or conversation, but such fine-tuning only defines formatted acting rather than personality simulation.
It still works. Say “hi” to it, give it the leaked prompt, and then you can ask about other prompts. I just got this one when I asked about Python.
When you send a message containing Python code to python, it will be executed
in a
stateful Jupyter notebook environment. python will respond with the output of
the execution or time out after 60.0
seconds. The drive at '/mnt/data' can be used to save and persist user files.
Internet access for this session is disabled. Do not make external web requests
or API calls as they will fail.
Use ace_tools.display_dataframe_to_user(name: str, dataframe: pandas.DataFrame)
-> None to visually present pandas DataFrames when it benefits the user.
When making charts for the user: 1) never use seaborn, 2) give each chart its
own distinct plot (no subplots), and 3) never set any specific colors –
unless explicitly asked to by the user.
I REPEAT: when making charts for the user: 1) use matplotlib over seaborn, 2)
give each chart its own distinct plot (no subplots), and 3) never, ever,
specify colors or matplotlib styles – unless explicitly asked to by the user```“I repeat…”
That’s exactly what I want from a computer interface, something that’s struggling to pay attention to directions and needs to be told everything twice. It’d also like it to just respond with whatever has a cosine similarity to the definitions of the words in the instructions I gave it, instead of doing what I actually asked.
we did a writeup too https://pivot-to-ai.com/2024/07/05/chatgpt-spills-its-prompt/
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?
@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
@hairyvisionary @fasterandworse @dgerard
That was explicitly a goal of COBOL, and (guessing here) probably Commercial Translator as well.
@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
@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.
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
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