Raw LLM Responses

I train and tune image diffusion models. Trying to get it to produce non-biased output is incredibly difficult, and can introduce new biases of their own. I try to work out biases by fine tuning with a variety of "races, faces and places" on the model itself. It's a balancing act, and more than once I've had model testers inform me that biases are pulling too hard and producing unexpected ethnic output. All that to say is, it's an ongoing process that will likely never be perfect. That said, Google's ham fisted attempts at forcing multicultural output into every prompt is problematic on it's own (Dalle3 does the same thing actually, though they were smart enough to put a filter in there for historical context, something Google should definitely adopt immediately, black "founding fathers" and Native American nazis is a bad look and is the dumbest kind of reverse whitewashing), the bigger problem I see is that Google's keyword filtering freaking out any time the word "white person" or "Hispanic person" is used, and is a fantastic example of corporate woke-ism going too far. OpenAI isn't immune from this, they've purposely detuned their model to produce more plastic looking and fake humans (to prevent deepfakes they say, results in shitty output tho), and they're filtering for nudity using ridiculous 1950's standards (go ahead, prompt "woman in a 2 piece bikini laying out on the beach", have fun getting that one out of Dalle3 - though if you add "wearing a burka" it'll work fine). End of the day, if you want good output free of ridiculous corporate culture/wokism/counter-culture nonsense, go open source. Stable Diffusion is free, it's easy to use, and can be run locally on minimal hardware or in the cloud for very cheap (or even free on a netbook if you get set up with the Stable Horde).