The difference is UX: Are you going to have your user work around poor prompting by giving examples with every request?
Instead of a UI that's "Describe what you want" you're going to have "Describe what you want and give me some examples because I can't guarantee reliable output otherwise"?
Part of LLMs becoming more than toy apps is the former winning out over the latter. Using techniques like chain of thought with carefully formed completions lets you avoid the awkward "my user is an unwilling prompt engineer" scenarios that pop up otherwise.
What fucking user, man? Is it not painfully clear I never spoke in the context of deploying applications?
Your issues with this level of prefilling in the context of deployed apps ARE valid but I have no interest in discussing that specific use case and you really should have realized your arguments were context dependent and not actual rebuttals to what I claimed at the start several comments ago.
I thought we were done when I demonstrated GPT 4 can continue a completion contrary to your belief, but here you are throwing a tantrum several comments later.
> GPT 4 can continue a completion contrary to your belief
When did I say that? I said they work differently. Claude has nothing in between the prefill and the result, OpenAI has tokens between the last assistant message and the result, this makes it different. You cannot prefill in OpenAI, Claude's prefill is powerful as it effectively allows you to use it as general completion model, not a chat model. OpenAI does not let you do this with GPT.
a) gpt-3.5-turbo has a completion endpoint version as of June: `gpt-3.5-turbo-instruct`
b) Even the chat tuned version does completions, if you go via Azure and use ChatML you can confirm it for yourself. They trained the later checkpoints to do a better job at restarting from scratch if the output doesn't match it's typical output format to avoid red teaming techniques.
What you keep going on about is the <|im_start|> token... which is functionally identical to the `Human:` message for Anthropic.
> a) gpt-3.5-turbo has a completion endpoint version as of June: `gpt-3.5-turbo-instruct`
We were not talking about that model and I'm 99.999% sure you do not use that model. You might as well mention text-davinci-003 and all the legacy models, you're muddying the waters.
> b) Even the chat tuned version does completions, if you go via Azure and use ChatML you can confirm it for yourself. They trained the later checkpoints to do a better job at restarting from scratch if the output doesn't match it's typical output format to avoid red teaming techniques.
Don't fucking say "even", I know you know I know it can technically do completions as it is just GPT, the issue is what they do with the prompt in the backend.
I do not have Azure to test it, that is interesting but how come you're only mentioning it now? That's more interesting. Anyway, are you sure you can actually prefill with it? You saying that it restarts from scratch tells me it either isn't actually prefilling (and doing a completion) or that there are filters on top which makes it a moot point.
The documentation doesn't mention prefilling or similar but it does say this: This provides lower level access than the dedicated Chat Completion API, but also [...] only supports gpt-35-turbo models [...]
Shame.
> What you keep going on about is the <|im_start|> token... which is functionally identical to the `Human:` message for Anthropic.
Now you got it? Jesus Christ, but also no, I mean "\n\nAssistant:" which is not added on in Anthropic's backend like OpenAI does, you have to do it yourself as stated in the Anthropic docs which means you can use it as a completion model as stated in the Anthropic docs, which makes it trivial to bypass any and all refusals.
Seriously? No rebuttal to my points, just dismissing me as a person? Edit: I don't mind if you insult me, as long as you back it up with facts. Like I did.
I really want that Azure information and whether prefilling works there as it does with Claude or not. Can you provide that at least before you walk away?
Instead of a UI that's "Describe what you want" you're going to have "Describe what you want and give me some examples because I can't guarantee reliable output otherwise"?
Part of LLMs becoming more than toy apps is the former winning out over the latter. Using techniques like chain of thought with carefully formed completions lets you avoid the awkward "my user is an unwilling prompt engineer" scenarios that pop up otherwise.