Semantic Sensitivities and Inconsistent Predictions: Measuring the Fragility of NLI Models
Erik A. ARAKELYAN, Zhaoqi Liu, Isabelle Augenstein · 2024
Recent studies of the emergent capabilities of transformer-based Natural Language Understanding (NLU) models have indicated that they have an understanding of lexical and compositional semantics.We provide evidence that suggests these claims should be taken with a grain of salt: we find that state-of-the-art Natural Language Inference (NLI) models are sensitive towards minor semantics preserving surface-form variations, which lead to sizable inconsistent model decisions during inference.Notably, this behaviour differs from valid and in-depth comprehension of compositional semantics, however does neither emerge when evaluating model accuracy on standard benchmarks nor when probing for syntactic, monotonic, and logically robust reasoning.We propose a novel framework to measure the extent of semantic sensitivity.To this end, we evaluate NLI models on adversarially generated examples containing minor semantics-preserving surface-form input noise.This is achieved using conditional text generation, with the explicit condition that the NLI model predicts the relationship between the original and adversarial inputs as a symmetric equivalence entailment.We systematically study the effects of the phenomenon across NLI models for in-and outof domain settings.Our experiments show that semantic sensitivity causes performance degradations of 12.92% and 23.71% average over in-and out-of-domain settings, respectively.We further perform ablation studies, analysing this phenomenon across models, datasets, and variations in inference and show that semantic sensitivity can lead to major inconsistency within model predictions.