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

arXiv: https://arxiv.org/abs/2402.14531 · HTML: https://mlsanity.com/paper/2402.14531/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 35 references that are in the map.
- First posted on arXiv 22 Feb 2024.

## Builds on (most cited first, 18 of 18)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [Teaching Machines to Read and Comprehend](https://mlsanity.com/paper/1506.03340/) (2015)
- [GLM: General Language Model Pretraining with Autoregressive Blank Infilling](https://mlsanity.com/paper/2103.10360/) (2021)
- [CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models](https://mlsanity.com/paper/2010.00133/) (2020)
- [C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models](https://mlsanity.com/paper/2305.08322/) (2023)
- [A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT](https://mlsanity.com/paper/2302.11382/) (2023)
- [BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language Generation](https://mlsanity.com/paper/2101.11718/) (2021)
- [XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages](https://mlsanity.com/paper/2106.13822/) (2021)
- [CLUE: A Chinese Language Understanding Evaluation Benchmark](https://mlsanity.com/paper/2004.05986/) (2020)
- [Assessing Cross-Cultural Alignment between ChatGPT and Human Societies: An Empirical Study](https://mlsanity.com/paper/2303.17466/) (2023)
- [Gender Bias in Masked Language Models for Multiple Languages](https://mlsanity.com/paper/2205.00551/) (2022)
- [CHBias: Bias Evaluation and Mitigation of Chinese Conversational Language Models](https://mlsanity.com/paper/2305.11262/) (2023)
- [Gender Bias in BERT -- Measuring and Analysing Biases through Sentiment Rating in a Realistic Downstream Classification Task](https://mlsanity.com/paper/2306.15298/) (2023)

## Built on by (most cited first, 30 of 41)

- [Survey of Cultural Awareness in Language Models: Text and Beyond](https://mlsanity.com/paper/2411.00860/) (2024)
- [MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model Evaluation](https://mlsanity.com/paper/2503.10497/) (2025)
- [Social Bias Evaluation for Large Language Models Requires Prompt Variations](https://mlsanity.com/paper/2407.03129/) (2024)
- [Does Tone Change the Answer? Evaluating Prompt Politeness Effects on Modern LLMs: GPT, Gemini, and LLaMA](https://mlsanity.com/paper/2512.12812/) (2025)
- [Firm or Fickle? Evaluating Large Language Models Consistency in Sequential Interactions](https://mlsanity.com/paper/2503.22353/) (2025)
- [Out of Style: RAG's Fragility to Linguistic Variation](https://mlsanity.com/paper/2504.08231/) (2025)
- [Prompting Science Report 1: Prompt Engineering is Complicated and Contingent](https://mlsanity.com/paper/2503.04818/) (2025)
- [Mind Your Tone: Investigating How Prompt Politeness Affects LLM Accuracy (short paper)](https://mlsanity.com/paper/2510.04950/) (2025)
- [Expect the Unexpected: FailSafe Long Context QA for Finance](https://mlsanity.com/paper/2502.06329/) (2025)
- [Interactional Fairness in LLM Multi-Agent Systems: An Evaluation Framework](https://mlsanity.com/paper/2505.12001/) (2025)
- [Minstrel: Structural Prompt Generation with Multi-Agents Coordination for Non-AI Experts](https://mlsanity.com/paper/2409.13449/) (2024)
- [Instability in Downstream Task Performance During LLM Pretraining](https://mlsanity.com/paper/2510.04848/) (2025)
- [The Struggles of LLMs in Cross-lingual Code Clone Detection](https://mlsanity.com/paper/2408.04430/) (2024)
- [Development and bilingual evaluation of Japanese medical large language model within reasonably low computational resources](https://mlsanity.com/paper/2409.11783/) (2024)
- [Traveling Across Languages: Benchmarking Cross-Lingual Consistency in Multimodal LLMs](https://mlsanity.com/paper/2505.15075/) (2025)
- [Humans overrely on overconfident language models, across languages](https://mlsanity.com/paper/2507.06306/) (2025)
- [M2M-Gen: A Multimodal Framework for Automated Background Music Generation in Japanese Manga Using Large Language Models](https://mlsanity.com/paper/2410.09928/) (2024)
- [ActionCOMET: A Zero-shot Approach to Learn Image-specific Commonsense Concepts about Actions](https://mlsanity.com/paper/2410.13662/) (2024)
- [Use Me Wisely: AI-Driven Assessment for LLM Prompting Skills Development](https://mlsanity.com/paper/2503.02532/) (2025)
- [A Japanese Language Model and Three New Evaluation Benchmarks for Pharmaceutical NLP](https://mlsanity.com/paper/2505.16661/) (2025)
- [Learning a Single Token to Replace Long System Prompts in LLMs](https://mlsanity.com/paper/2511.23271/) (2025)
- [Chasing the Public Score: User Pressure and Evaluation Exploitation in Coding Agent Workflows](https://mlsanity.com/paper/2604.20200/) (2026)
- [CIE: Controlling Language Model Text Generations Using Continuous Signals](https://mlsanity.com/paper/2505.13448/) (2025)
- [Small Talk, Big Impact: The Energy Cost of Thanking AI](https://mlsanity.com/paper/2601.22357/) (2026)
- [Caring Without Feeling: Affective Dynamics as the Control Layer of Human-AI Agent Collaboration](https://mlsanity.com/paper/2606.18259/) (2026)
- [Effectiveness of Zero-shot-CoT in Japanese Prompts](https://mlsanity.com/paper/2503.06765/) (2025)
- [Pragmatics beyond humans: meaning, communication, and LLMs](https://mlsanity.com/paper/2508.06167/) (2025)
- [LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents](https://mlsanity.com/paper/2509.02241/) (2025)
- [ADAB: Arabic Dataset for Automated Politeness Benchmarking -- A Large-Scale Resource for Computational Sociopragmatics](https://mlsanity.com/paper/2602.13870/) (2026)
- [Imperative Interference: Social Register Shapes Instruction Topology in Large Language Models](https://mlsanity.com/paper/2603.25015/) (2026)

## Abstract (arXiv)

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.