You're absolutely correct, in that it's never guaranteed what the next token is.
My pushback is limited to that the theoretical maximal degenerate behavior described in either of your comments is highly improbable in practice, with a lot of givens, such as reasonable parameters, reasonable model.
I.e. it will not
- give totally different answers due to seed changing.
- end up X% of the time, where X > 5 say it is impossible, and the other (100 - X)%, provide some solution.
I have integrated with GPT3.0/GPT3.5/GPT4 and revisions thereof via API, as well as Claude 2 and this week, Claude 3. I wrote a native inference solution that runs, among others, StableLM Zephyr 3B, Mistral 7B, and Mixtral 8x7B, and I wrote code that does inference, step by excruciating step, in a loop, on web via WASM, and via C++, tailored solutions for Android, iOS, macOS, Android, and Windows.
I still think it depends on the subject you are prompting. If LLM knows that thing very well it will stick to the answer, otherwise it can go in a different direction based on how different initial assessment was.
Yesterday I asked it to write a simple VB script to show a reminder that I will schedule via command line using Windows task schedular. In first attempt it suggested to create VB file for each message based on initial reasoning that I can not pass arguments to VB file like that. It didn't seem correct (confirmed via Google) then resubmitted the same prompt but this time it said that I can simply pass my reminder message as arg to VB script and the next code was based on that. (I don't know VB or ever used task schedular before)
This was GPT-4. You are not wrong about 'maximal degenerate behaviour' but initially generated assumption can lead to different answers overall. Chain of thought prompting stems from this exact behaviour.
Comments
You're absolutely correct, in that it's never guaranteed what the next token is.
My pushback is limited to that the theoretical maximal degenerate behavior described in either of your comments is highly improbable in practice, with a lot of givens, such as reasonable parameters, reasonable model.
I.e. it will not
- give totally different answers due to seed changing.
- end up X% of the time, where X > 5 say it is impossible, and the other (100 - X)%, provide some solution.
I have integrated with GPT3.0/GPT3.5/GPT4 and revisions thereof via API, as well as Claude 2 and this week, Claude 3. I wrote a native inference solution that runs, among others, StableLM Zephyr 3B, Mistral 7B, and Mixtral 8x7B, and I wrote code that does inference, step by excruciating step, in a loop, on web via WASM, and via C++, tailored solutions for Android, iOS, macOS, Android, and Windows.
I still think it depends on the subject you are prompting. If LLM knows that thing very well it will stick to the answer, otherwise it can go in a different direction based on how different initial assessment was.
Yesterday I asked it to write a simple VB script to show a reminder that I will schedule via command line using Windows task schedular. In first attempt it suggested to create VB file for each message based on initial reasoning that I can not pass arguments to VB file like that. It didn't seem correct (confirmed via Google) then resubmitted the same prompt but this time it said that I can simply pass my reminder message as arg to VB script and the next code was based on that. (I don't know VB or ever used task schedular before)
This was GPT-4. You are not wrong about 'maximal degenerate behaviour' but initially generated assumption can lead to different answers overall. Chain of thought prompting stems from this exact behaviour.