Consider the listwise loss formula used for training on ranked preferences:

$\mathcal{L} = -\mathbb{E}\left[\frac{1}{N(N-1)}\sum_{\substack{\mathbf{y}_a, \mathbf{y}_b \in Y \mathbf{y}_a\neq \mathbf{y}_b}} \log\Pr(\mathbf{y}_a \succ \mathbf{y}_b|\mathbf{x})\right]$

True or False: If a model is completely uncertain about the preferences within a ranked list (i.e., it assigns $\Pr(\mathbf{y}_a \succ \mathbf{y}_b|\mathbf{x}) = 0.5$ for all distinct pairs), the contribution of that specific lis