Logit Inflation in ListMLE: Theoretical Analysis and Mitigation Strategies

Modern learning-to-rank methods often rely on listwise objectives that directly model and optimize relative document order over entire permutations. While these objectives improve ranking quality, they frequently produce models with highly inflated relevance scores whose magnitudes exceed what is necessary for meaningful document separation, leading to poor probabilistic calibration.