since accuracies aren’t near-100% we know there are some cases the model hasn’t memorized, so the mechanism you suggest doesn’t apply to those inputs
That makes sense.
I suspect the prompts are a bigger deal
Do you suppose a suitable proxy for prompt quality can be replicating these experiments with LLM debaters/judges of different sizes? Let’s say P is the optimal prompt and Q is a suboptimal one, then LLM performance with prompt Q ⇐ LLM performance with prompt P ⇐ bigger LLM performance with prompt Q.
That makes sense.
Do you suppose a suitable proxy for prompt quality can be replicating these experiments with LLM debaters/judges of different sizes? Let’s say P is the optimal prompt and Q is a suboptimal one, then LLM performance with prompt Q ⇐ LLM performance with prompt P ⇐ bigger LLM performance with prompt Q.