用知识图谱和逻辑推理让大模型输出可解释，提升高风险场景可信度
Building Trustworthy AI: Transparent AI Systems via Large Language Models, Ontologies, and Logical Reasoning (TranspNet)
- 融合专家知识与逻辑推理框架，增强大模型输出的结构化解释能力
- 通过检索增强生成与形式化验证，提升结果准确率与可追溯性
- 适合医疗、金融等需高可信度的落地场景
随着人工智能在医疗、金融等高风险领域应用日益广泛，其缺乏透明性的问题引发关注。尽管大语言模型（LLMs）能生成高精度结果，但其“黑箱”特性制约了可解释性与信任度。为此，本文提出TranspNet系统，将符号化AI与大模型结合：利用领域专家知识、检索增强生成（RAG）以及答案集编程（ASP）等形式化推理框架，对大模型输出进行结构化推理与验证。该方法旨在使AI系统输出既准确又可解释，满足监管对透明性与责任归属的要求。TranspNet为开发可靠且可解释的AI系统提供了可行路径，适用于对信任度要求极高的实际应用场景。
原文摘要 · Abstract (English)
Growing concerns over the lack of transparency in AI, particularly in high-stakes fields like healthcare and finance, drive the need for explainable and trustworthy systems. While Large Language Models (LLMs) perform exceptionally well in generating accurate outputs, their "black box" nature poses significant challenges to transparency and trust. To address this, the paper proposes the TranspNet pipeline, which integrates symbolic AI with LLMs. By leveraging domain expert knowledge, retrieval-augmented generation (RAG), and formal reasoning frameworks like Answer Set Programming (ASP), TranspNet enhances LLM outputs with structured reasoning and verification.This approach strives to help AI systems deliver results that are as accurate, explainable, and trustworthy as possible, aligning with regulatory expectations for transparency and accountability. TranspNet provides a solution for developing AI systems that are reliable and interpretable, making it suitable for real-world applications where trust is critical.
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