Speaking in Words, Thinking in Logic: A Dual-Process Framework in QA Systems

• URL: http://arxiv.org/abs/2507.20491v1
• Date: Mon, 28 Jul 2025 03:00:35 GMT
• Title: Speaking in Words, Thinking in Logic: A Dual-Process Framework in QA Systems
• Authors: Tuan Bui, Trong Le, Phat Thai, Sang Nguyen, Minh Hua, Ngan Pham, Thang Bui, Tho Quan,
• Abstract summary: Text-JEPA is a framework for converting natural language into first-order logic (NL2FOL)<n>We show that Text-JEPA achieves competitive performance with significantly lower computational overhead compared to larger LLM-based systems.<n>Our findings highlight the potential of structured, interpretable reasoning frameworks for building efficient and explainable QA systems in specialized domains.
• Score: 0.602276990341246
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in large language models (LLMs) have significantly enhanced question-answering (QA) capabilities, particularly in open-domain contexts. However, in closed-domain scenarios such as education, healthcare, and law, users demand not only accurate answers but also transparent reasoning and explainable decision-making processes. While neural-symbolic (NeSy) frameworks have emerged as a promising solution, leveraging LLMs for natural language understanding and symbolic systems for formal reasoning, existing approaches often rely on large-scale models and exhibit inefficiencies in translating natural language into formal logic representations. To address these limitations, we introduce Text-JEPA (Text-based Joint-Embedding Predictive Architecture), a lightweight yet effective framework for converting natural language into first-order logic (NL2FOL). Drawing inspiration from dual-system cognitive theory, Text-JEPA emulates System 1 by efficiently generating logic representations, while the Z3 solver operates as System 2, enabling robust logical inference. To rigorously evaluate the NL2FOL-to-reasoning pipeline, we propose a comprehensive evaluation framework comprising three custom metrics: conversion score, reasoning score, and Spearman rho score, which collectively capture the quality of logical translation and its downstream impact on reasoning accuracy. Empirical results on domain-specific datasets demonstrate that Text-JEPA achieves competitive performance with significantly lower computational overhead compared to larger LLM-based systems. Our findings highlight the potential of structured, interpretable reasoning frameworks for building efficient and explainable QA systems in specialized domains.

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