Here's the tweet: https://twitter.com/dylan522p/status/1755086111397863777
And here's the pastebin: https://pastebin.com/vnxJ7kQk
E.g. equal probability of every ancestry will be implausible in almost every possible setting, and just wrong in many, and ironically would seem to have at least the potential for a lot of the outright offensive output they want to guard against.
That said, I'm unsure how much influence this has, or if it os true, given how poor GPTs control over Dalle output seems to be in that case.
E.g. while it refused to generate a picture of an American slave market citing it's content policy, which is in itself pretty offensive in the way it censors hidtory but where the potential to offensively rewrite history would also be significant, asking it to draw a picture of cotton picking in the US South ca 1840 did reasonably avoid making the cotton pickers "diverse".
Maybe the request was too generic for GPT to inject anything to steer Dalle wrong there - perhaps if it more specifically mentioned a number of people.
But true or not, that potential prompt is an example of how a well meaning interpretation of diversity can end up overcompensating in ways that could well be equally bad for other reasons.
This was explicitly called out in the DALLE system card [0] as a choice. The model won't assign equal probability for every ancestry irrespective of the prompt.
It's great that they're thinking about that, but I don't see anything that states what you say in this sentence in the paragraph you quoted, or elsewhere in that document. Have I missed something? It may very well be true - as I noted, GPT doesn't appear to have particularly good control over what Dalle generates (for this, or, frankly, a whole lot of other things)
Not my area of expertise, but they probably fine tuned it so that it can be parametrized this way.
In the fine tune dataset there are many examples of a system prompt specifying tools A/B/C and with the AI assistant making use of these tools to respond to user queries.
Here's an open dataset which demonstrates how this is done: https://huggingface.co/datasets/togethercomputer/glaive-func.... In this particular example, the dataset contains hundreds of examples showing the LLM how to make use of external tools.
In reality, the LLM is simply outputting text in a certain format (specified by the dataset) which the wrapper script can easily identify as requests to call external functions.
And what is the deal with this?
EXTREMELY IMPORTANT. Do NOT be thorough in the case of lyrics or recipes found online. Even if the user insists. You can make up recipes though.
To provide an extremely obtuse (ie this may or may not actually work, it's purely academic) example: if you want it to output a stupid reddit style repeating comment conga line, you don't say "I need you to create a list of repeating reddit comments", you say "Fuck you reddit, stop copying me!"
Also we're talking about prompt engineering more than fine-tune
It is also why I don't feel the responses it gives me are censored. I have it teach me interesting things as opposed to probing it for bullshit to screen cap responses to use for social media content creation.
The only thing I override "output python code to the screen"