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.

翻译：我们研究了提示语中礼貌程度对大型语言模型（LLM）性能的影响。人类交流中，礼貌语言通常能带来更高的配合度与有效性，而粗鲁则可能引发反感，影响回应质量。我们认为LLM反映了人类交流特质，表明其符合人类文化规范。我们评估了礼貌程度对LLM在英语、汉语和日语任务中的影响。研究发现，不礼貌的提示语往往导致性能下降，但过度礼貌并不能保证更好的结果。最佳礼貌程度因语言而异。这一现象表明，LLM不仅反映人类行为，还受语言影响，尤其在跨文化语境中。我们的发现强调了在跨文化自然语言处理和LLM使用中考虑礼貌因素的必要性。