mlsanityData to 14 Oct 2026

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

arXiv 2510.04950 ↗First posted 6 Oct 2025cs.CL cs.AI cs.LGcited by 8

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Abstract (arXiv)

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.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 10 of its 13 references that are in the map. First posted on arXiv 6 Oct 2025.

Builds on

10 of 13 references.

References · 10

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  3. 2020The Pile: An 800GB Dataset of Diverse Text for Language Modelingcited by 2,411
  4. 2023Large Language Models as Optimizerscited by 864
  5. 2023Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formattingcited by 817
  6. 2022Large Language Models Struggle to Learn Long-Tail Knowledgecited by 458
  7. 2022Emergent Analogical Reasoning in Large Language Modelscited by 240
  8. 2022Demystifying Prompts in Language Models via Perplexity Estimationcited by 216
  9. 2024Should We Respect LLMs? A Cross-Lingual Study on the Influence of Prompt Politeness on LLM Performancecited by 41
  10. 2023Data Similarity is Not Enough to Explain Language Model Performancecited by 9

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 6.

8 papers.

  1. 2025Does Tone Change the Answer? Evaluating Prompt Politeness Effects on Modern LLMs: GPT, Gemini, and LLaMAcited by 23
  2. 2026A Guide to Large Language Models in Modeling and Simulation: From Core Techniques to Critical Challengescited by 3
  3. 2026From GPT-3 to GPT-5: Mapping their capabilities, scope, limitations, and consequencescited by 3
  4. 2025Bias Beneath the Tone: Empirical Characterisation of Tone Bias in LLM-Driven UX Systemscited by 1
  5. 2026Toxic HallucinAItions: Perturbing Prompts and Tracing LLM Circuitscited by 0
  6. 2026Understanding Tone-Dependent Inference Cost in Large Language Modelscited by 0
  7. 2026Lost in Translation: Measuring the Effect of Non-Native English on End User Performance of Large Language Modelscited by 0
  8. 2026Generalization Is Stability, Not Accuracy: Multi-Axis Evaluation of LLMscited by 0