3 ms·

Any idea how this is done? I.e. is it just priming the underlying GPT model with plugin information additionally to the user input ("you can ask Wolfram Alpha b

by elaus 4y ago

Any idea how this is done? I.e. is it just priming the underlying GPT model with plugin information additionally to the user input ("you can ask Wolfram Alpha by replying 'hey wolfram: ...' ") and performing API calls when the GPT model returns certain keywords ('hey wolfram: $1')?

trzy 4y ago
Yup, basically. Edit: see here: https://github.com/openai/chatgpt-retrieval-plugin/blob/main/services/extract_metadata.py https://github.com/openai/chatgpt-retrieval-plugin/blob/main... I did this a while back with ARKit: https://github.com/trzy/ChatARKit/blob/17fca768ce8abd39fb27d75e752c8abb7bd380a6/iOS/ChatARKit/ChatARKit/Engine/Engine.swift#L50 https://github.com/trzy/ChatARKit/blob/17fca768ce8abd39fb27d...
- elaus 4y ago
  Thanks, very interesting! Weird that it never occurred to me before reading OpenAI's announcement (and missing all the cool projects like yours beforehand).
  - trzy 4y ago
    On the bright side, now you’re basically caught up. This stuff is shockingly easy :)
brap 4y ago
I wonder, can these instructions be revealed with prompt injection?
- int_19h 4y ago
  Everything that is in the context window can be potentially revealed with prompt engineering. (In this case, there's no prompt injection to speak of because letting the user input an arbitrary request is part of the UI. I think it's more accurate to call it "injection" only when that's not anticipated, like when Bing picks up instructions from the webpage you tell it to summarize.)
  - brap 4y ago
    I guess that's technically true, but then what would you call tricking the model to reveal its instructions via anticipated input?
georgehm 4y ago
I like to think that to get a sense of how this might be done, one way maybe to extrapolate from this experiment at https://til.simonwillison.net/llms/python-react-pattern https://til.simonwillison.net/llms/python-react-pattern .

by elaus 4y ago

trzy 4y ago

Yup, basically. Edit: see here: https://github.com/openai/chatgpt-retrieval-plugin/blob/main/services/extract_metadata.py https://github.com/openai/chatgpt-retrieval-plugin/blob/main... I did this a while back with ARKit: https://github.com/trzy/ChatARKit/blob/17fca768ce8abd39fb27d75e752c8abb7bd380a6/iOS/ChatARKit/ChatARKit/Engine/Engine.swift#L50 https://github.com/trzy/ChatARKit/blob/17fca768ce8abd39fb27d...

elaus 4y ago
Thanks, very interesting! Weird that it never occurred to me before reading OpenAI's announcement (and missing all the cool projects like yours beforehand).
- trzy 4y ago
  On the bright side, now you’re basically caught up. This stuff is shockingly easy :)

brap 4y ago

I wonder, can these instructions be revealed with prompt injection?

int_19h 4y ago
Everything that is in the context window can be potentially revealed with prompt engineering. (In this case, there's no prompt injection to speak of because letting the user input an arbitrary request is part of the UI. I think it's more accurate to call it "injection" only when that's not anticipated, like when Bing picks up instructions from the webpage you tell it to summarize.)
- brap 4y ago
  I guess that's technically true, but then what would you call tricking the model to reveal its instructions via anticipated input?

georgehm 4y ago

I like to think that to get a sense of how this might be done, one way maybe to extrapolate from this experiment at https://til.simonwillison.net/llms/python-react-pattern https://til.simonwillison.net/llms/python-react-pattern .