Should We Respect LLMs? A Cross-Lingual Study on the Influence of Prompt Politeness on LLM Performance
This work addresses the need to consider politeness in cross-cultural natural language processing and LLM usage, though it is incremental in exploring cultural influences on model behavior.
The study examined how politeness levels in prompts affect large language model performance across English, Chinese, and Japanese tasks, finding that impolite prompts often lead to poor results, but optimal politeness varies by language without guaranteeing better outcomes.
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.