用道德规则解释大模型生成中的不确定性，提升可信度。
Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation
- 基于道德原则设计规则系统，应对认知与随机不确定性。
- 在临床与法律场景中，规则系统显著提升信任与可解释性。
- 轻量级逻辑引擎实现透明推理，适合高风险应用。
大型语言模型（LLMs）在高风险场景中应用日益广泛，解释不确定性既涉及技术也关乎伦理。现有概率方法常缺乏透明性且与用户期望不符。本文提出一种基于规则的道德原则框架，用于处理LLM生成文本中的不确定性。借鉴道德心理学与德性伦理，定义了谨慎、谦逊、责任等规则，以指导在知识性或随机性不确定性下的响应。这些规则被编码于轻量级Prolog引擎中，根据不确定性水平（低、中、高）触发对应系统动作，并附带自然语言解释。通过情景模拟评估规则覆盖度、公平性与信任校准。在临床与法律场景中的应用案例表明，道德推理可有效增强信任与可解释性。该方法为社会负责任的自然语言生成提供了一种透明、轻量的替代方案。
原文摘要 · Abstract (English)
Large language models (LLMs) are increasingly used in high-stakes settings, where explaining uncertainty is both technical and ethical. Probabilistic methods are often opaque and misaligned with expectations of transparency. We propose a framework based on rule-based moral principles for handling uncertainty in LLM-generated text. Using insights from moral psychology and virtue ethics, we define rules such as precaution, deference, and responsibility to guide responses under epistemic or aleatoric uncertainty. These rules are encoded in a lightweight Prolog engine, where uncertainty levels (low, medium, high) trigger aligned system actions with plain-language rationales. Scenario-based simulations benchmark rule coverage, fairness, and trust calibration. Use cases in clinical and legal domains illustrate how moral reasoning can improve trust and interpretability. Our approach offers a transparent, lightweight alternative to probabilistic models for socially responsible natural language generation.
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