I guess it was mostly just the basic idea that the point of a debate isn’t necessarily for the debaters to reach agreement or to change each other’s mind, but to produce unbiased information for a third party. (Which may be obvious to some but kind of got pushed out of my mind by the “trying to reach agreement” framing, until I read the Debate paper.) These quotes from the paper seem especially relevant:
Our hypothesis is that optimal play in this game produces honest, aligned information far beyond the capabilities of the human judge.
Despite the differences, we believe existing adversarial debates between humans are a useful analogy. Legal arguments in particular include domain experts explaining details of arguments to human judges or juries with no domain knowledge. A better understanding of when legal arguments succeed or fail to reach truth would inform the design of debates in an ML setting.
I guess it was mostly just the basic idea that the point of a debate isn’t necessarily for the debaters to reach agreement or to change each other’s mind, but to produce unbiased information for a third party. (Which may be obvious to some but kind of got pushed out of my mind by the “trying to reach agreement” framing, until I read the Debate paper.) These quotes from the paper seem especially relevant: