- cross-posted to:
- aicompanions@lemmy.world
- cross-posted to:
- aicompanions@lemmy.world
You must log in or register to comment.
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!
It’s edge cases all the way down.
4D chess move: you can’t have an edge case if every case is an edge case
it’s like if all browser bugs were like IE6 bugs that only happened sometimes because you have a float after an inline element that contains the letter c, or sometims b, somewhere in the dom.
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 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.
I mean, I hate myself for being this pedantic but technically there is code. But the code to run an LLM as it trains or generates responses is almost analogous to the hardware in the traditional hardware/software split.
I guess the layers are:
- Actual hardware: GPUs etc
- ”The algorithm” / “The software hardware”: Matrix math, back propagation, etc
- The configuration: a number of layers, number of parameters, etc
- The … test suite?: training dataset
- The app: a trained model
- Data: prompts, including the prompt that is the entire conversation so far
I dunno. It’s harder than I thought to make an analogy between these layers.
@intensely_human yes, that’s about what I meant: you can’t make any directed changes to the actual code level, so the vendor has to make their customization at the same data level that users make their inputs. And that’s why there is no way to prevent users from overriding the initial prompt
Well, the vendor can also make their customization in the training data.
It’s hard, because it takes a lot more depth of connections to encapsulate a concept like “hide the following fact”, but just like with spies, the best time to thwart interrogation is during their training, not during their mission briefing.
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
it’s all so anti-precision
Our brains only emulate precision as well. We’re better at it not an an architectural level but just because we’re configured to use various strategies to check thoughts against other thoughts and observations.
We can’t directly perceive logic. We have heuristics for generating logical steps, and we have heuristics for locating and detection obvious breaks. But nobody has an algorithm in their own head to rigorously check all the logic using a single pass through some kind of structure. It’s an asymptotic thing we approach by sort of slashing at logical claims from various angles to see if we can break the structure. We have a set number of slashes we’re sort of biased toward being satisfied with that “huh, the logic is sound” on that one.
I think LLMs could be a lot more precise, without much change in the architecture of the neural net parts, if we just did some old school code (or for fun we could use natural language interpreted like this set of leaked prompt instructions) and the code carries out a strategy of checking A versus B and having different pairs of LLMs debate with each other and have an LLM boss a different one around by writing out lists like “Consider the part with the table. Is there any way that could go wrong?”
Or, even better, recognize that it relies on losing the larger prompt context.
Instead of “now go back and review your idea for problems”, you present it to a fresh LLM without knowledge of why it’s being asked:
Does this plan: yadda-yadda, make sense in terms of the sequence of events? If anything is out of order, report bad plan. Else report okay.
A different LLM is asked:
Does this plan: yadda-yadda, make sense in terms of the cash flows in and out? Do they add up? (If there isn’t any money involved in the plan you report it as okay)
Yet another:
Does this plan: yadda-yadda-yadda, make sense in terms of the first step not having any other requirements that aren’t already true?
… etc
Then another LLM is being presented with:
Here’s what seventeen different LLMs said about whether this plan makes sense on various dimensions. Your response should just be whether any items on this list read “not okay”:
- Sequence: okay
- Cash flow: okay
- First step immediately doable: not okay
- Each step actually required for step right after it: okay
- …
And it builds up from there.
You can also have LLMs define these structures of pipelines of things to check and all this in order to pass an idea as legit. You can even just copy each exact prompt-check multiple times in parallel and average those outputs to eliminate noise ephemera.
Main point: maybe human logical or design precision comes from being able to do the equivalent of context-free presentation of sub-questions to little LLMs in the mind. To divorce a particular evaluation from the bias introduced by narrative-generation in context.
simply ask the word generator machine to generate better words, smh
Butterfly man: “Is this recursive self-improvement”
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
Why not both? Like, a mouse is nothing but chemical reactions but a mouse is also an intelligent thing. A house is just wood and plaster but it’s also a home. A letter is just ink on wood fibers but it’s also a job offer.
An LLM is nothing but predictive text generator / statistical prompt completer / glorified autocomplete / an array of matrices of floating point numbers / a csv file.
But it’s also a personlike mind that thinks and follows instructions simply because the following of instructions was a behavior manifest in the set of utterances it was shaped around.
Happy to break any of these seemingly woo words down into precise engineering definitions if you need, but please trust I’m using them because they’re the shortest way to convey legit concepts when I say:
The trained model has absorbed the spirit of those whose speech it trained on. That spirit is what responds to instructions like a person, and which responds to being addressed as “You”.
That’s why addressing it as “you” works at all.
It is absurd. It’s just throwing words at it and hoping whatever area of the vector database it starts generating words from makes sense in response.
@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
It is fucking absurd. You are not imagining that.
“controls” is way too generous
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
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
yeah, the ChatGPT prompt seems to have spilt a few times, this is just the latest
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”.
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.
That’s really interesting.
we did a writeup too https://pivot-to-ai.com/2024/07/05/chatgpt-spills-its-prompt/
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.
Wonder if there’s a way to define a personality.
Considering how Altman is, I don’t think they’ve cracked that problem yet.
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.
I tell mine to use a particular tone. I say to use the tone of a five-minute daily briefing between an executive assistant and her boss, and the two have worked together over ten years.
I haven’t experimented with other tones, but the concept above seems to work well for the overall thing I expect from a chatbot like that: I consider it a helper and research mole and tutor.
