Comment by magnio
14 hours ago
I saw on Twitter that in an ML course at Tsinghua University, one of the tests asks students to write quizzes that fail the most LLM models as possible.
What if we create a benchmark that works like this and assigns ELO scores? Models fight head-to-head by writing a question, a bug, or an incomplete implementation, which the opponent has to answer, fix, or finish.
We could call this "generative adversarial network" (GAN) :)
https://en.wikipedia.org/wiki/Generative_adversarial_network
This kind of approach would generally still need human guidance, otherwise these models might get stuck in weird niche corners of the problem space that would not be relevant to any real world project.
We could call this "reinforcement learning from human feedback" (RLHF) :)
https://en.wikipedia.org/wiki/Reinforcement_learning_from_hu...
How do you prevent degenerate strategies? I could trivially give a model a SHA256 hash and ask it to provide the source input.
In class you'd probably want a rule saying at least one LLM should be able to figure out the answer, but in a head-to-head I'm not sure how to solve it.
Maybe make the LLM:s write questions that they can solve (without seeing the question writing context) but not other LLm:s.
On the other hand then maybe a good strategy would be to write questions that the LLM just happen to have in a nich dataset in its training ”what did user5455 say to user6835?”
Nevermind my idea.
Who knows. Maybe Mythos 5 already found a hole in SHA256, so this won't be too hard. :)
That was Fudan I think