# No Universal Courtesy: A Cross-Linguistic, Multi-Model Study of Politeness Effects on LLMs Using the PLUM Corpus

arXiv: https://arxiv.org/abs/2604.16275 · HTML: https://mlsanity.com/paper/2604.16275/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 77 references that are in the map.
- First posted on arXiv 17 Apr 2026.

## Builds on (most cited first, 13 of 13)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond](https://mlsanity.com/paper/2304.13712/) (2023)
- [A Computational Approach to Politeness with Application to Social Factors](https://mlsanity.com/paper/1306.6078/) (2013)
- [Let the LLMs Talk: Simulating Human-to-Human Conversational QA via Zero-Shot LLM-to-LLM Interactions](https://mlsanity.com/paper/2312.02913/) (2023)
- [Ethical Reasoning and Moral Value Alignment of LLMs Depend on the Language we Prompt them in](https://mlsanity.com/paper/2404.18460/) (2024)
- [Should We Respect LLMs? A Cross-Lingual Study on the Influence of Prompt Politeness on LLM Performance](https://mlsanity.com/paper/2402.14531/) (2024)
- [An In-depth Look at Gemini's Language Abilities](https://mlsanity.com/paper/2312.11444/) (2023)
- [Interpreting Neural Networks to Improve Politeness Comprehension](https://mlsanity.com/paper/1610.02683/) (2016)
- [A Comparison of DeepSeek and Other LLMs](https://mlsanity.com/paper/2502.03688/) (2025)
- [Comparative Analysis Based on DeepSeek, ChatGPT, and Google Gemini: Features, Techniques, Performance, Future Prospects](https://mlsanity.com/paper/2503.04783/) (2025)
- [Comparing human and LLM politeness strategies in free production](https://mlsanity.com/paper/2506.09391/) (2025)
- [LLMs in Education: Novel Perspectives, Challenges, and Opportunities](https://mlsanity.com/paper/2409.11917/) (2024)
- [Polite or Direct? Conversation Design of a Smart Display for Older Adults Based on Politeness Theory](https://mlsanity.com/paper/2203.15767/) (2022)

## Abstract (arXiv)

This paper explores the response of Large Language Models (LLMs) to user prompts with different degrees of politeness and impoliteness. The Politeness Theory by Brown and Levinson and the Impoliteness Framework by Culpeper form the basis of experiments conducted across three languages (English, Hindi, Spanish), five models (Gemini-Pro, GPT-4o Mini, Claude 3.7 Sonnet, DeepSeek-Chat, and Llama 3), and three interaction histories between users (raw, polite, and impolite). Our sample consists of 22,500 pairs of prompts and responses of various types, evaluated across five levels of politeness using an eight-factor assessment framework: coherence, clarity, depth, responsiveness, context retention, toxicity, conciseness, and readability. The findings show that model performance is highly influenced by tone, dialogue history, and language. While polite prompts enhance the average response quality by up to ~11% and impolite tones worsen it, these effects are neither consistent nor universal across languages and models. English is best served by courteous or direct tones, Hindi by deferential and indirect tones, and Spanish by assertive tones. Among the models, Llama is the most tone-sensitive (11.5% range), whereas GPT is more robust to adversarial tone. These results indicate that politeness is a quantifiable computational variable that affects LLM behaviour, though its impact is language- and model-dependent rather than universal. To support reproducibility and future work, we additionally release PLUM (Politeness Levels in Utterances, Multilingual), a publicly available corpus of 1,500 human-validated prompts across three languages and five politeness categories, and provide a formal supplementary analysis of six falsifiable hypotheses derived from politeness theory, empirically assessed against the dataset.