P-CoT：一种面向大语言模型音系推理的、以教学法为动机的参与式思维链提示方法
P-CoT: A Pedagogically-motivated Participatory Chain-of-Thought Prompting for Phonological Reasoning in LLMs
AI总结：
本文提出以教学法为动机的参与式思维链提示P-CoT，通过支架式教学和发现式学习等结构化引导激活大语言模型的潜在音系推理能力，在PhonologyBench上最高提升52%，并在部分任务中超越人类基线。
摘要：
本研究探讨了基于文本的大语言模型（LLMs）在音系推理方面的潜力。利用PhonologyBench基准，我们评估了押韵词生成、字素到音素（g2p）转换和音节计数等任务。我们对12个大语言模型的评估表明，虽然少样本学习带来的提升并不稳定，但引入一种新颖的以教学法为动机的参与式思维链（P-CoT）提示——该方法以支架式教学和发现式学习等教育理论为基础——能够持续提升性能。该方法利用结构化引导来激活潜在的音系能力，最高实现了52%的提升，并在某些任务中甚至超越了人类基线。未来工作可以致力于针对特定模型优化P-CoT提示，或探索其在不同语言领域的应用。
英文摘要：
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.
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。
