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this post was submitted on 06 Oct 2023
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Do you know how much data these models are actually trained on? Do you really think it's all specifically parsed for tone?
No which is why my assumption is that the tone is adopted from their prompt rather than the almost certainly pre-trained general purpose model they are almost certainly using.
Right, and that statement itself is a massive oversimplification of the process. I feel like I've explained that in detail many times already.
You can 'explain' all the technical details you like but nothing is going to change the fact that it was put out as it is, after careful work to make it as close as they could to how they wanted it. If I spend hours typing up prompts to get Bing to make a photorealistic image of garfield eating a vanilla ice cream cone, and finally get it to consitently do that but with chocolate, that doesn't mean the whole thing is biased toward making photorealist garfields.
Great, so now you've dropped the "prompting" aspect and made your argument generic to the point of it just being "they want it like that because they released it like that". Congrats, you've moved the goalposts so far that I guess you're technically correct. Good job?
I didn't drop the prompting. over half that comment is specifically an analogy about prompting. are you ok
Your analogy has absolutely nothing to do with how LLMs are trained. You seem to think GPT is just prompt engineering...