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"Making stuff up and being confidently wrong are well known side-effects of LLMs and there are many techniques to change this behavior." I didn't know there ar
by antman 4y ago
"Making stuff up and being confidently wrong are well known side-effects of LLMs and there are many techniques to change this behavior."
I didn't know there are many techniques to mitigate this
- microjim 4y agoI agree with you principally, and generally, but in rather small domains like this I would imagine symptom management using negative examples (i.e. training pairs where the response is a refusal to answer) and adding more explicit statements about what is not true, possible, or known to the corpus would get you to a pretty good place.
- visarga 4y ago> I didn't know there are many techniques to mitigate this A trivial idea - you can use GPT-3 to inject bullshit/hallucinations into real text. Then train the model to solve the reverse task, of detecting bullshit in input text.
- stavros 4y agoHow is it going to detect whether a given URL is real, though?
- codetrotter 4y agoGive the AI access to cURL and train it on how to interpret responses. What could possibly go wrong :p
- stavros 4y agoI've been thinking of doing that for some stuff! Teach it how to call APIs and see what happens.
- thornewolf 4y agoI did exactly that! See [1] for my implementation of a telegram bot that is able to write and execute python code. [1] https://github.com/thornewolf/gpt-3-execution https://github.com/thornewolf/gpt-3-execution
- fragmede 4y agoJust give it the return code (200 vs 404 vs NXDOMAIN) and avoid creating Skynet.
- wongarsu 4y agoIf you use a few-shot technique (i.e. your prompt contains a couple of example questions and answers) you can mitigate this behavior by adding a question with the answer "I don't know". More generally, if you teach the model to reject nonsense questions and admit if it doesn't know something it's more likely to do that