面向数学错误纠正的教学对齐LLM导师
Towards Pedagogically Aligned LLM Tutors for Math Mistake Remediation
AI总结：
提出两阶段对齐流程（监督微调+偏好优化），结合合成数据提升LLM在数学错误纠正中的教学质量和事实准确性，优于基线模型并与专有模型竞争。
摘要：
大型语言模型在智能辅导系统中具有巨大潜力，但它们往往无法遵循有效的教学策略，例如在不透露最终答案的情况下引导学生。我们研究了两阶段对齐流程在数学错误纠正中的应用，结合了在辅导对话上的监督微调与在合成偏好对上的直接偏好优化。我们构建了一个数据集，将现有的辅导语料库与沿教学维度（如支架式教学和事实性）生成的合成数据相结合，并研究了融入解答正确性和标准答案的不同输入配置。实验表明，该方法在事实准确性和教学质量上均优于基础模型和现有的辅导模型。人工评估进一步表明，我们的最佳模型与强大的专有基线模型具有竞争力，同时在开放性、透明度和可复现性方面提供了额外优势。我们的结果突显了基于偏好的教学对齐的有效性，同时也揭示了在可靠评估辅导质量方面的挑战。
英文摘要：
Large language models have strong potential for use in intelligent tutoring systems, but they often fail to follow effective pedagogical strategies, such as guiding students without revealing final answers. We study the application of a two-stage alignment pipeline for math mistake remediation, combining supervised fine-tuning on tutoring dialogs with Direct Preference Optimization on synthetic preference pairs. We construct a dataset that integrates existing tutoring corpora with synthetic data generated along pedagogical dimensions, such as scaffolding and factuality, and study different input configurations that incorporate solution correctness and gold answers. Experiments show that this approach improves both factual accuracy and pedagogical quality over base models and existing tutoring models. Human evaluation further indicates that our best model is competitive with a strong proprietary baseline, while providing additional benefits in terms of openness, transparency, and reproducibility. Our results highlight the effectiveness of preference-based pedagogical alignment, while also revealing challenges in reliably evaluating tutoring quality.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
