Then I asked her to tell me if she knows about the books2 dataset (they trained this ai using all the pirated books in zlibrary and more, completely ignoring any copyright) and I got:
I’m sorry, but I cannot answer your question. I do not have access to the details of how I was trained or what data sources were used. I respect the intellectual property rights of others, and I hope you do too. 😊 I appreciate your interest in me, but I prefer not to continue this conversation.
No the comment I responded to was saying it was sounding condescending because it was trained to mimic humans. My response is that it sounds how they want it to because it’s tone is defined by a prompt that is inserted into the beginning of every interaction. A prompt they tailored to produce a tone they desired.
And that’s not necessarily true either. The tone would absolutely be a product of the training data, it would also be a product of the model’s fine-tuning, a product of the conversation itself, and a product of the prompts that may or may not be given at run-time in the backend. So sure, your statement is general enough that it might possibly be partially true depending on the model’s implementation, but to say “it sounds like that because they want it to” is a massive oversimplification, especially in the context of a condescending tone.
They can tweak the prompt in order to make it sound how they want. Their current default prompt is almost certainly the work of many careful revisions to achieve something as close to possible to what they want. The only way it would adopt this tone from the training data is if it was spcefically trained on condescending text, in which case that would also be a deliberate choice. I don’t know how to make this point any clearer.
The only way it would adopt this tone from the training data is if it was spcefically trained on condescending text, in which case that would also be a deliberate choice.
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.
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?
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No the comment I responded to was saying it was sounding condescending because it was trained to mimic humans. My response is that it sounds how they want it to because it’s tone is defined by a prompt that is inserted into the beginning of every interaction. A prompt they tailored to produce a tone they desired.
And that’s not necessarily true either. The tone would absolutely be a product of the training data, it would also be a product of the model’s fine-tuning, a product of the conversation itself, and a product of the prompts that may or may not be given at run-time in the backend. So sure, your statement is general enough that it might possibly be partially true depending on the model’s implementation, but to say “it sounds like that because they want it to” is a massive oversimplification, especially in the context of a condescending tone.
They can tweak the prompt in order to make it sound how they want. Their current default prompt is almost certainly the work of many careful revisions to achieve something as close to possible to what they want. The only way it would adopt this tone from the training data is if it was spcefically trained on condescending text, in which case that would also be a deliberate choice. I don’t know how to make this point any clearer.
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