AQE-RF: An Adaptive Quantifier Extension and Rule-Filtering Graph Network for Logical Reasoning of Text.

Abstract

Logical reasoning of text requires neural models to possess strong contextual comprehension and logical reasoning ability to draw conclusions from limited information. To improve the logical reasoning capabilities of pretrained language models (PLMs), existing approaches can be broadly categorized into neural architecture-based methods and large language model (LLM)-driven strategies. While neural methods struggle with fine-grained logic that fails to capture detailed semantic roles and constraints, LLM-driven approaches, despite generating multistep reasoning sequences, lack explicit inference control and suffer from error accumulation due to their implicit and stochastic nature. Some works have tried using logical expressions, like first-order logic, but these approaches often fail to handle quantifiers systematically or support clear reasoning processes. Inspired by first-order logic and generalized quantifier (GQ) theory, we propose AQE-RF, a model based on an adaptive quantifier extension and rule-filtering graph network to address this challenge. The first component constructs a fine-grained text logical graph (FTLG) and then performs GQ instantiation based on option attention. The second component performs rule-filtered deductive reasoning, using conflict scores and dynamic programming (DP) to select coherent, interpretable inference paths. Extensive experiments on the LogiQA, ReClor, and AR-LSAT datasets demonstrate the effectiveness and robustness of AQE-RF.