用教育理论设计提示词，让大模型更好做语音推理。
P-CoT: A Pedagogically-motivated Participatory Chain-of-Thought Prompting for Phonological Reasoning in LLMs
- 基于支架式学习设计分步引导提示
- 最高提升52%，部分任务超越人类表现
- 适合语音处理、语言教育等场景
本研究探索文本型大语言模型（LLMs）在语音推理方面的潜力。利用PhonologyBench基准测试，评估押韵词生成、音素转写（g2p）和音节计数等任务。对12个LLMs的评估显示，少样本学习效果不稳定；而引入一种基于教育理论（如支架式教学、发现学习）的新型参与式链式思维提示（P-CoT），能持续提升性能。该方法通过结构化引导激活模型隐含的语音能力，在某些任务上实现最高52%的提升，甚至超过人类基准。未来工作可针对特定模型优化P-CoT提示，或拓展至其他语言领域。
原文摘要 · Abstract (English)
This study explores the potential of phonological reasoning within text-based large language models (LLMs). Utilizing the PhonologyBench benchmark, we assess tasks like rhyme word generation, g2p conversion, and syllable counting. Our evaluations across 12 LLMs reveal that while few-shot learning offers inconsistent gains, the introduction of a novel Pedagogically-motivated Participatory Chain-of-Thought (P-CoT) prompt, which is anchored in educational theories like scaffolding and discovery learning, consistently enhances performance. This method leverages structured guidance to activate latent phonological abilities, achieving up to 52% improvement and even surpassing human baselines in certain tasks. Future work could aim to optimize P-CoT prompts for specific models or explore their application across different linguistic domains.
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