High-quality conversational datasets are crucial for the successful development of Intelligent Tutoring Systems (ITS) that utilize a Large Language Model (LLM) backend. Synthetic student-teacher dialogues, generated using advanced GPT-4 models, are a common strategy for creating these datasets. However, subjects like physics that entail complex calculations pose a challenge. While GPT-4 presents impressive language processing capabilities, its limitations in fundamental mathematical reasoning curtail its efficacy for such subjects. To tackle this limitation, we introduce in this paper an innovative stateful prompt design. Our design orchestrates a mock conversation where both student and tutorbot roles are simulated by GPT-4. Each student response triggers an internal monologue, or `code soliloquy' in the GPT-tutorbot, which assesses whether its subsequent response would necessitate calculations. If a calculation is deemed necessary, it scripts the relevant Python code and uses the Python output to construct a response to the student. Our approach notably enhances the quality of synthetic conversation datasets, especially for subjects that are calculation-intensive. Our preliminary Subject Matter Expert evaluations reveal that our Higgs model, a fine-tuned LLaMA model, effectively uses Python for computations, which significantly enhances the accuracy and computational reliability of Higgs' responses. Code, models, and datasets is available at https://github.com/luffycodes/Tutorbot-Spock-Phys.

翻译：高质量对话数据集对于开发基于大型语言模型（LLM）后端的智能辅导系统（ITS）至关重要。利用先进GPT-4模型生成的合成型师生对话是创建此类数据集的常见策略。然而，涉及复杂计算的物理等学科带来了特殊挑战。尽管GPT-4展现出卓越的语言处理能力，但其在基础数学推理方面的局限性制约了它对这类学科的教学效能。为克服这一局限，本文提出了一种创新的状态化提示设计。该设计编排了一场由GPT-4同时模拟学生与辅导机器人角色的模拟对话。每次学生应答都会触发GPT辅导机器人内部的"代码独白"（code soliloquy）式自我对话，用以评估其后续回复是否需要计算支持。若判定需要计算，系统将编写相应的Python代码，并利用Python输出结果构建对学生问题的回复。我们的方法显著提升了合成对话数据集的质量，尤其适用于计算密集型学科。初步学科专家评估表明，基于微调LLaMA模型的"希格斯"（Higgs）模型能有效利用Python执行运算，这显著增强了希格斯模型响应的准确性和计算可靠性。相关代码、模型及数据集已开源发布于https://github.com/luffycodes/Tutorbot-Spock-Phys。