Should We Respect LLMs? A Cross-Lingual Study on the Influence of Prompt
  Politeness on LLM Performance
Ziqi Yin Hao Wang Kaito Horio Daisuke Kawahara Satoshi Sekine
We investigate the impact of politeness levels in prompts on the performance
of large language models (LLMs). Polite language in human communications often
garners more compliance and effectiveness, while rudeness can cause aversion,
impacting response quality. We consider that LLMs mirror human communication
traits, suggesting they align with human cultural norms. We assess the impact
of politeness in prompts on LLMs across English, Chinese, and Japanese tasks.
We observed that impolite prompts often result in poor performance, but overly
polite language does not guarantee better outcomes. The best politeness level
is different according to the language. This phenomenon suggests that LLMs not
only reflect human behavior but are also influenced by language, particularly
in different cultural contexts. Our findings highlight the need to factor in
politeness for cross-cultural natural language processing and LLM usage.