[N] Inverse Scaling Prize: $250k in prizes for finding tasks where larger language models do worse

We're used to finding that task performance scales well with large increases in sizes of language models. But for real-world applications, it's also very meaningful to search for failure cases preemptively to fix the underlying issues. Can you find and convincingly demonstrate these failure cases where language models scale *inversely*, with larger models behaving worse?

You don't necessarily need to have extra deep knowledge of ML or language models in order to participate and win, because all models are frozen …

language language models machinelearning scaling