Saved in:
| Main Author: | |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.13787 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916334896414720 |
|---|---|
| author | Sato, Keisuke |
| author_facet | Sato, Keisuke |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_13787 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The Honorific Effect: Exploring the Impact of Japanese Linguistic Formalities on AI-Generated Physics Explanations Sato, Keisuke Physics Education Computation and Language 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. |
| title | The Honorific Effect: Exploring the Impact of Japanese Linguistic Formalities on AI-Generated Physics Explanations |
| topic | Physics Education Computation and Language |
| url | https://arxiv.org/abs/2407.13787 |