แนวคิดหลัก
The core message of this work is to apply causal effect estimation strategies to measure the effect of context interventions (whose effect on the entailment label is mediated by the semantic monotonicity characteristic) and interventions on the inserted word-pair (whose effect on the entailment label is mediated by the relation between these words) in order to investigate the robustness and sensitivity of Transformer-based NLI models to relevant and irrelevant changes.
บทคัดย่อ
This paper presents a causal analysis of Transformer-based natural language inference (NLI) models, focusing on a structured subset of the NLI task based on natural logic. The authors construct a causal diagram that captures the desired and undesired potential reasoning routes that may describe model behavior.
The key contributions are:
Extending previous work on causal analysis of NLP models, the authors investigate a structured sub-problem in NLI and present a causal diagram that captures both desired and undesired potential reasoning routes.
They adapt the NLI-XY dataset to a meaningful collection of intervention sets, enabling the computation of certain causal effects.
They calculate estimates for undesired direct causal effects and desired total causal effects, which serve as a quantification of model robustness and sensitivity to the intermediate semantic features of interest.
They compare a suite of BERT-like NLI models, identifying behavioral weaknesses in high-performing models and behavioral advantages in some worse-performing ones.
The results show that similar benchmark accuracy scores may be observed for models that exhibit very different behavior, especially concerning specific semantic reasoning patterns and higher-level properties such as robustness and sensitivity to target features. The causal analysis complements previous observations of model biases and provides a quantitative perspective on the flow of information through semantic variables (or lack thereof) in the models.
สถิติ
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คำพูด
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