用模糊逻辑让大模型自适应调整，提升教学与交互中的安全性和灵活性。
A Fuzzy Logic Prompting Framework for Large Language Models in Adaptive and Uncertain Tasks
- 基于人类学习理论设计可调节的提示框架
- 在模拟教学中显著提升指导质量与适应性
- 无需微调即可实现可解释的智能响应，适合教育等复杂场景
我们提出一种模块化提示框架，支持大语言模型在动态、以用户为中心的任务中更安全、更自适应地使用。该方法基于人类学习理论，特别是近侧发展区（ZPD），结合自然语言边界提示与编码了模糊支架逻辑和自适应规则的控制架构。该设计使大模型能根据用户状态调节行为，无需微调或外部调度。在模拟智能辅导环境中，该框架在多个模型上提升了支架质量、适应性及教学一致性，优于标准提示基线。评估通过基于评分规则的大语言模型评分器大规模进行。尽管最初针对教育领域开发，该框架在游戏过程内容生成等高交互场景中也展现出潜力。设计注重安全部署，提供了一种可复用的方法，用于在不确定或演变的上下文中构建可解释、目标对齐的模型行为。
原文摘要 · Abstract (English)
We introduce a modular prompting framework that supports safer and more adaptive use of large language models (LLMs) across dynamic, user-centered tasks. Grounded in human learning theory, particularly the Zone of Proximal Development (ZPD), our method combines a natural language boundary prompt with a control schema encoded with fuzzy scaffolding logic and adaptation rules. This architecture enables LLMs to modulate behavior in response to user state without requiring fine-tuning or external orchestration. In a simulated intelligent tutoring setting, the framework improves scaffolding quality, adaptivity, and instructional alignment across multiple models, outperforming standard prompting baselines. Evaluation is conducted using rubric-based LLM graders at scale. While initially developed for education, the framework has shown promise in other interaction-heavy domains, such as procedural content generation for games. Designed for safe deployment, it provides a reusable methodology for structuring interpretable, goal-aligned LLM behavior in uncertain or evolving contexts.
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