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0 pointsianbutler3y ago0 comments

Interoperability can also be achieved with small adapters written for the prompting style of the particular model being interfaced with, I'd be surprised if like LangChain or AutoGPT don't already do something like this in their systems.

I'm currently building something that leverages an ensemble of different LLMs depending on the difficulty of a task and ran into this issue.

Dolly V2 takes "###Instruction: <your stuff> ###Response" as the structure fed to the model where as GPT3.5 Turbo wasn't trained to treat that particular structure as important.

The nice thing is that GPT3.5 Turbo will just roll with the prompt structure Dolly uses but that only works in very large LLMs, I'd imagine I wouldn't get away with it in other 12BN parameter models.

But realistically this could look like taking the "INSTRUCTION MEMORY EXAMPLE [COMPLETION]" schema represented in a library and each adapter would transform it into

"MEMORY EXAMPLE INSTRUCTION [COMPLETION]" schema or whatever is needed by the different model.

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killthebuddha3y ago

I think I agree, and am doing something similar as well. Even the adapters approach does require some amount of consistency across prompts. For example, if you have one prompt that says “you are a very helpful assistant” and one prompt that says “you are a very lazy assistant”, then even if those prompts are otherwise written to be as orthogonal as possible you will still probably see degradation in completion quality.

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