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This preregistered replication tests whether the monotonicity effect on label agreement in NLI generalizes from selected low-agreement items to unselected populations, finding that the effect reverses and is small, suggesting the earlier finding was conditional on selection.
This paper measures how much formal semantic structure explains human label variation in natural language inference (NLI) using ChaosNLI data, finding group-level effects on entropy but item-level ceilings and null composition effects.
This paper introduces neural slack variables, a primal-side approach that converts constraint enforcement into a regression problem by coupling the primary network with a jointly learned auxiliary network, achieving zero violations on monotonicity and convexity tests and enabling arbitrage-free learning of volatility surfaces.