Testing Pre-trained Language Models' Understanding of Distributivity via Causal Mediation Analysis

• URL: http://arxiv.org/abs/2209.04761v1
• Date: Sun, 11 Sep 2022 00:33:28 GMT
• Title: Testing Pre-trained Language Models' Understanding of Distributivity via Causal Mediation Analysis
• Authors: Pangbo Ban, Yifan Jiang, Tianran Liu, Shane Steinert-Threlkeld
• Abstract summary: We introduce DistNLI, a new diagnostic dataset for natural language inference. We find that the extent of models' understanding is associated with model size and vocabulary size.
• Score: 13.07356367140208
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To what extent do pre-trained language models grasp semantic knowledge regarding the phenomenon of distributivity? In this paper, we introduce DistNLI, a new diagnostic dataset for natural language inference that targets the semantic difference arising from distributivity, and employ the causal mediation analysis framework to quantify the model behavior and explore the underlying mechanism in this semantically-related task. We find that the extent of models' understanding is associated with model size and vocabulary size. We also provide insights into how models encode such high-level semantic knowledge.

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