标题： 用语言表达，用逻辑思考：问答系统中的双进程框架 显示英文标题

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

摘要： 近年来，大型语言模型（LLMs）的进展显著提升了问答（QA）能力，特别是在开放领域的情境中。 然而，在教育、医疗和法律等封闭领域场景中，用户不仅需要准确的答案，还需要透明的推理过程和可解释的决策机制。 虽然神经符号（NeSy）框架已被视为一种有前景的解决方案，结合了LLMs的自然语言理解和符号系统的形式推理，但现有方法通常依赖于大规模模型，并在将自然语言转化为形式逻辑表示方面表现出效率低下。 为了解决这些限制，我们引入了Text-JEPA（基于文本的联合嵌入预测架构），这是一种轻量且有效的框架，用于将自然语言转化为一阶逻辑（NL2FOL）。 受双系统认知理论的启发，Text-JEPA通过高效生成逻辑表示来模拟系统1，而Z3求解器则作为系统2，实现强大的逻辑推理。 为了严格评估NL2FOL到推理的流程，我们提出了一种综合评估框架，包括三个自定义指标：转换分数、推理分数和Spearman rho分数，它们共同捕捉逻辑翻译的质量及其对推理准确性下游影响。 在特定领域的数据集上的实证结果表明，与更大的LLM系统相比，Text-JEPA实现了具有竞争力的性能，同时计算开销显著降低。 我们的研究结果突显了结构化、可解释的推理框架在构建专用领域高效且可解释的QA系统方面的潜力。 显示英文摘要

摘要： 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.