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Should We Respect LLMs? A Cross-Lingual Study on the Influence of Prompt Politeness on LLM Performance

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arxiv 2402.14531 v2 pith:UIL5Y5NL submitted 2024-02-22 cs.CL

classification cs.CL
keywords languagellmspolitenesshumanperformancepromptsculturaldifferent
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Japanese Language Model and Three New Evaluation Benchmarks for Pharmaceutical NLP

    cs.CL 2025-05 conditional novelty 6.0 of 10

    A continually pretrained 7B Japanese pharmaceutical LLM outperforms open medical models on new Japanese pharma benchmarks, while all models, including GPT-4o, fail at cross-sentence consistency checks.

  2. LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents

    cs.AI 2025-09 reject novelty 5.0 of 10

    On CUAD legal contracts, a prompt-engineered QWEN-2 pipeline with chunking and two answer-selection heuristics reportedly outperforms the fine-tuned DeBERTa-large baseline by about 9%, reaching claimed state-of-the-ar...

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