HackerHACKER PAPER · 2025 · arXiv preprint
Mind Your Tone: Investigating How Prompt Politeness Affects LLM Accuracy
What question does this paper answer?
Does a multiple-choice question get a different answer when its tone changes? The authors use Deep Research to generate 50 base questions and answer keys, rewrite each into five tone variants, and primarily evaluate ChatGPT-4o. Ruder variants score higher on this set. This is a small wording experiment: the rewrites also change words and length, so it does not isolate an emotional response by the model or establish abusive prompting as a general recommendation.
What it contributes
- 01
The experiment compares five phrasings of the same 50 questions using four-option answer accuracy. Its 250 prompt variants are not 250 independent base questions.
- 02
Table 2 reports mean accuracy of 80.8% for very polite prompts and 84.8% for very rude prompts, a four-percentage-point difference. The authors apply paired t-tests to accuracy across ten runs; these are reported results for this dataset and prompting setup.
- 03
The paper describes its experiments as preliminary and leaves the mechanism of wording sensitivity unresolved. It mentions tests of other models without reporting comparably complete result tables for them.
Check the original passages
Pages count from the first page of the PDF file. Page jumps depend on the source viewer; section names and short quotes provide another way to find the passage.
This suggests
that the tone does matter.the dataset size
is relatively smallGuide source: arXiv v1 preprint ↗ · unknown
Read this paper alongside other sources
These readings connect the paper with specific arguments from talks and other sources.
Hacker News comments 9
Verified mentions linking to this paper, newest first. This is not the complete comment tree of any one submission. Recommendations, references, and criticism are labeled separately; comments remain in their original language.
• https://arxiv.org/abs/2510.04950 — Mind Your Tone: Investigating How Prompt Politeness Affects LLM Accuracy
• https://arxiv.org/abs/2402.14531 — Should We Respect LLMs? A Cross-Lingual Study on the Influence of Prompt Politeness on LLM Performance
• https://arxiv.org/abs/2505.17332 — SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use
> 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.
https://arxiv.org/abs/2510.04950
Like you I generally prefer polite phrasings over rude ones, and I'm certainly not going to tell you how you should communicate to LLM models, however it's interesting to note that being somewhat rude and to the point seems to have some objective advantages, see for example:
Mind Your Tone: Investigating How Prompt Politeness Affects LLM Accuracy (short paper)
https://arxiv.org/abs/2510.04950
Understanding Tone-Dependent Inference Cost in Large Language Models
https://arxiv.org/abs/2607.23915
I would hate for this style of communication to leak through to daily communications with humans (e.g. colleagues) though, so even just for that it can make sense to hold on to a polite form of communication :-).
https://arxiv.org/pdf/2510.04950
It was published a while ago. But I wonder if it still holds true today.
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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% 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".
> impolite prompts consistently outperformed polite ones, with accuracy ranging from 80.8% for Very Polite prompts to 84.8% for Very Rude prompts.
Direct submissions and discussions (4)
Investigating how prompt politeness affects LLM accuracy (2025) · 156 points · 2026-05-26 ↗Mind Your Tone: Investigating How Prompt Politeness Affects LLM Accuracy · 1 points · 2026-01-09 ↗Investigating How Prompt Politeness Affects LLM Accuracy · 4 points · 2025-10-31 ↗Impolite LLM prompts consistently outperform polite ones · 5 points · 2025-10-11 ↗