The Honorific Effect: Exploring the Impact of Japanese Linguistic Formalities on AI-Generated Physics Explanations

TL;DR

This research examines how Japanese honorifics influence AI-generated physics explanations, revealing that linguistic formality affects response quality, style, and cultural adaptation in large language models.

Contribution

It demonstrates that honorifics significantly impact AI responses, highlighting the importance of cultural linguistic cues in AI explanation quality and educational applications.

Findings

Honorifics affect AI response formality and depth.

Different models respond variably to honorifics.

Cultural linguistic cues influence AI explanation style.

Abstract

This study investigates the influence of Japanese honorifics on the responses of large language models (LLMs) when explaining the law of conservation of momentum. We analyzed the outputs of six state-of-the-art AI models, including variations of ChatGPT, Coral, and Gemini, using 14 different honorific forms. Our findings reveal that honorifics significantly affect the quality, consistency, and formality of AI-generated responses, demonstrating LLMs' ability to interpret and adapt to social context cues embedded in language. Notable variations were observed across different models, with some emphasizing historical context and derivations, while others focused on intuitive explanations. The study highlights the potential for using honorifics to adjust the depth and complexity of AI-generated explanations in educational contexts. Furthermore, the responsiveness of AI models to cultural linguistic elements underscores the importance of considering cultural factors in AI development for educational applications. These results open new avenues for research in AI-assisted education and cultural adaptation in AI systems, with significant implications for personalizing learning experiences and developing culturally sensitive AI tools for global education.