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Mind Your Tone: Investigating How Prompt Politeness Affects LLM Accuracy (short paper)

Om Dobariya, Akhil Kumar

2025 · arXiv

Language modelsStatistics & probabilityMathematics
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From the abstract

The wording of natural language prompts has been shown to influence the performance of large language models (LLMs), yet the role of politeness and tone remains underexplored. In this study, we investigate how varying levels of prompt politeness affect model accuracy on multiple-choice questions. We created a dataset of 50 base questions spanning mathematics, science, and history, each rewritten into five tone variants: Very Polite, Polite, Neutral, Rude, and Very Rude, yielding 250 unique prompts. Using ChatGPT 4o, we evaluated responses across these conditions and applied paired sample t-tests to assess statistical significance. Contrary to expectations, impolite prompts consistently outperformed polite ones, with accuracy ranging from 80.8% for Very Polite prompts to 84.8% for Very Rude prompts. These findings differ from earlier studies that associated rudeness with poorer outcomes, suggesting that newer LLMs may respond differently to tonal variation. Our results highlight the importance of studying pragmatic aspects of prompting and raise broader questions about the social dimensions of human-AI interaction.

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5 selected excerpts · 5 total mentions

sebastiennight
Neutral mention · rule
> Terseness doesn't offend the statistical model, but giving platitudes may color its output My understanding of the state of current research is that the impact of tone/politeness on performance is highly model-dependent, and language dependent as well. See `No Universal Courtesy` (April 2026) https://arxiv.org/html/2604.16275v1 While polite prompts enhance the average response quality by upto 11% and impolite tones worsen it, these effects are neither consistent nor universal across languages and models. English is best served by courtesy or direct, Hindi by deferential and indirect and Spanish by assertive. Among the models, Llama is the most tone-sensitive (11.5%…
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range), but GPT is more robust to adversarial tone. Or `Mind Your Tone` (Oct 2025) https://arxiv.org/abs/2510.04950 Contrary to expectations, impolite prompts consistently outperformed polite ones, with accuracy ranging from 80.8% for Very Polite prompts to 84.8% for Very Rude prompts. I think the best conclusion is to interact in a way that is efficient for you, gives you the level of results you're looking for, and mostly importantly does not progressively degrade your relationship and interactions with other humans. Hence the typical advice to just default to "corporate polite".
Original comment Sol Loves to Cheat
Xmd5a
Neutral mention · rule
https://arxiv.org/abs/2510.04950 > impolite prompts consistently outperformed polite ones, with accuracy ranging from 80.8% for Very Polite prompts to 84.8% for Very Rude prompts.
Original comment Claude Fable 5
alentred
Neutral mention · rule
Reminds me of this study: https://arxiv.org/pdf/2510.04950 . It demonstrates that being "rude" or "very rude" increases the accuracy of the results. A dubious but very fun read. The prompts in Table 1 (top of page 3) are awesome. I am sure they tried other prompts, but didn't include them to the paper.
Original comment The User Is Visibly Frustrated

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