用提示工程教大模型学乐理，效果比不给上下文高近50%。
Teaching LLMs Music Theory with In-Context Learning and Chain-of-Thought Prompting: Pedagogical Strategies for Machines
- 用上下文提示和逐步推理引导大模型理解乐理概念。
- 有上下文时，Claude在MEI格式下正确率达75%，显著优于无上下文的52%。
- 适合音乐教育者、AI音乐工具开发者参考教学策略。
本研究评估了大型语言模型（如ChatGPT、Claude、Gemini）通过上下文提示与思维链提示学习乐理知识的能力。采用精心设计的上下文提示和分步示例，探索如何引导模型掌握日益复杂的乐理内容，并检验人类教学策略在机器教育中的适用性。评测基于加拿大皇家音乐学院RCM六级考试题，涵盖音程与和弦识别、调性判断、终止式分类及节拍分析等。同时评估了ABC、Humdrum、MEI、MusicXML等音乐编码格式的适用性。所有实验均在有无上下文提示条件下进行。结果显示，无上下文时ChatGPT搭配MEI表现最佳（52%），有上下文时Claude搭配MEI表现最佳（75%）。未来工作将优化提示并扩展至更高级乐理内容。该研究有助于理解如何有效教学大模型，对教育者、学生及AI音乐工具开发者均有应用价值。
原文摘要 · Abstract (English)
This study evaluates the baseline capabilities of Large Language Models (LLMs) like ChatGPT, Claude, and Gemini to learn concepts in music theory through in-context learning and chain-of-thought prompting. Using carefully designed prompts (in-context learning) and step-by-step worked examples (chain-of-thought prompting), we explore how LLMs can be taught increasingly complex material and how pedagogical strategies for human learners translate to educating machines. Performance is evaluated using questions from an official Canadian Royal Conservatory of Music (RCM) Level 6 examination, which covers a comprehensive range of topics, including interval and chord identification, key detection, cadence classification, and metrical analysis. Additionally, we evaluate the suitability of various music encoding formats for these tasks (ABC, Humdrum, MEI, MusicXML). All experiments were run both with and without contextual prompts. Results indicate that without context, ChatGPT with MEI performs the best at 52%, while with context, Claude with MEI performs the best at 75%. Future work will further refine prompts and expand to cover more advanced music theory concepts. This research contributes to the broader understanding of teaching LLMs and has applications for educators, students, and developers of AI music tools alike.
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