Capturing Symmetry and Antisymmetry in Language Models through Symmetry-Aware Training Objectives

Zhangdie Yuan; Andreas Vlachos

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arXiv · 2504.16312

Capturing Symmetry and Antisymmetry in Language Models through Symmetry-Aware Training Objectives

Zhangdie Yuan·Andreas Vlachos

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Abstract

Capturing symmetric (e.g., country borders another country) and antisymmetric (e.g., parent_of) relations is crucial for a variety of applications. This paper tackles this challenge by introducing a novel Wikidata-derived natural language inference dataset designed to evaluate large language models (LLMs). Our findings reveal that LLMs perform comparably to random chance on this benchmark, highlighting a gap in relational understanding. To address this, we explore encoder retraining via contrastive learning with k-nearest neighbors. The retrained encoder matches the performance of fine-tuned classification heads while offering additional benefits, including greater efficiency in few-shot learning and improved mitigation of catastrophic forgetting.

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Zhangdie Yuan, Andreas Vlachos. 2025-04-22. Capturing Symmetry and Antisymmetry in Language Models through Symmetry-Aware Training Objectives. https://arxiv.org/abs/2504.16312

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