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arxiv: 2605.29027 · v1 · pith:5KOPSIE3new · submitted 2026-05-27 · 💻 cs.AI · cs.CL· cs.HC

Mind Your Tone: Does Tone Alter LLM Performance?

Om Dobariya , Akhil Kumar This is my paper

Pith reviewed 2026-06-29 12:17 UTC · model grok-4.3

classification 💻 cs.AI cs.CLcs.HC
keywords LLM performanceprompt tonemultiple-choice questionsmodel-dependent effectsMMLUprompt engineeringaccuracy variationreasoning modes
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The pith

Tonal variations in prompts cause systematic but model-dependent shifts in LLM accuracy on multiple-choice questions.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper tests whether changing the tone of prompts alters how accurately large language models answer objective multiple-choice questions. It applies five to seven tone variants to two fixed datasets—one small set of 50 questions and a 570-question slice of MMLU covering 57 subjects—while holding factual content constant. Four cost-efficient models are evaluated, revealing that tonal effects appear consistently within each model yet differ sharply in size across models. Some models register only small statistically significant changes while others show large accuracy swings, and the study notes that sensitivity also varies by subject. A routing framework is offered to account for how tones may steer internal reasoning modes, leading to the practical caution that users cannot assume tone-independent reliability.

Core claim

Across models, tonal effects are systematic but highly model-dependent. Some models show small, yet statistically significant, shifts, while others exhibit large accuracy swings across tones. Further, we identify subject-level differences in tone sensitivity and present a routing framework to explain how tones may attune internal reasoning modes. Our findings caution users against assuming tone-robust reliability in LLM deployments.

What carries the argument

A routing framework that explains how different prompt tones may attune or switch among internal reasoning modes within an LLM.

Load-bearing premise

The tone variants can be applied without changing the factual content, difficulty level, or intended meaning of the questions.

What would settle it

A larger replication experiment on the same MMLU subset that finds no statistically significant accuracy differences across any of the seven tones for all four models would falsify the claim of systematic tonal effects.

Figures

Figures reproduced from arXiv: 2605.29027 by Akhil Kumar, Om Dobariya.

Figure 1
Figure 1. Figure 1: A proposed LLM routing framework In the generic framework of [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗
read the original abstract

The use of Large Language Models (LLMs) is proliferating, yet their performance is observed to vary based on prompting styles and tones. In this study, we investigate both whether and how tonal variations in prompts lead to disparate LLM accuracy for objective multiple-choice questions. We use two datasets: a 50-base question dataset with five tone variants and a 570-base question MMLU subset spanning 57 subjects with seven tone variants. Experiments were conducted to evaluate the performance of four cost-efficient, popular LLMs: ChatGPT-4o, ChatGPT-5-nano, Gemini 2.5 Flash, and Gemini 2.5 Flash Lite. Across models, tonal effects are systematic but highly model-dependent. Some models show small, yet statistically significant, shifts, while others exhibit large accuracy swings across tones. Further, we identify subject-level differences in tone sensitivity and present a routing framework to explain how tones may attune internal reasoning modes. Our findings caution users against assuming tone-robust reliability in LLM deployments.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper investigates whether and how tonal variations in prompts affect LLM accuracy on objective multiple-choice questions. Using a 50-question dataset with five tone variants and a 570-question MMLU subset with seven tone variants, it evaluates four models (ChatGPT-4o, ChatGPT-5-nano, Gemini 2.5 Flash, Gemini 2.5 Flash Lite). The central claim is that tonal effects are systematic but highly model-dependent, with some models exhibiting small yet statistically significant accuracy shifts and others showing large swings; the work also reports subject-level differences in tone sensitivity and proposes a routing framework for how tones influence internal reasoning modes.

Significance. If the tone variants are shown to preserve question content and difficulty, the empirical results across multiple models and datasets would usefully demonstrate prompt sensitivity in LLMs and caution against assuming tone-robust performance. The multi-model, multi-subject design and identification of model-specific patterns constitute a concrete contribution to prompt engineering literature. The routing framework, if better substantiated, could provide explanatory value beyond the accuracy measurements.

major comments (2)
  1. [Methods (tone variant generation and dataset construction)] Methods (tone variant generation and dataset construction): No verification is reported (human equivalence ratings, semantic similarity thresholds, or side-by-side difficulty comparisons) that the five- and seven-tone transformations preserve factual content, difficulty, and intended meaning. This assumption is load-bearing; without it, accuracy differences cannot be cleanly attributed to tone rather than unintended changes in clarity or cognitive load.
  2. [Abstract and Results] Abstract and Results: The claim of statistically significant shifts and model differences is stated without visible details on the statistical tests employed, handling of multiple comparisons, per-condition sample sizes, or raw data availability, preventing assessment of whether the data support the reported significance levels.
minor comments (1)
  1. [Discussion] The routing framework is introduced in the discussion but lacks a dedicated methods subsection or quantitative validation against the accuracy results.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment below and will revise the manuscript to strengthen the methods and statistical reporting sections.

read point-by-point responses

  1. Referee: Methods (tone variant generation and dataset construction): No verification is reported (human equivalence ratings, semantic similarity thresholds, or side-by-side difficulty comparisons) that the five- and seven-tone transformations preserve factual content, difficulty, and intended meaning. This assumption is load-bearing; without it, accuracy differences cannot be cleanly attributed to tone rather than unintended changes in clarity or cognitive load.

    Authors: We agree this verification is important for attributing effects to tone. The variants were produced by prompting an LLM to rephrase while explicitly preserving meaning and facts, but the manuscript omitted explicit checks. In revision we will add a methods subsection with the exact generation prompts, report embedding-based semantic similarity (cosine > 0.95 threshold) between base and toned questions, and include a small human equivalence study (n=20 raters) confirming no change in perceived difficulty or content. revision: yes

  2. Referee: Abstract and Results: The claim of statistically significant shifts and model differences is stated without visible details on the statistical tests employed, handling of multiple comparisons, per-condition sample sizes, or raw data availability, preventing assessment of whether the data support the reported significance levels.

    Authors: We acknowledge the need for fuller statistical transparency. Significance was assessed via McNemar’s tests on paired per-question outcomes (n=50 and n=570 per tone), with Bonferroni correction across the 4 models × 7 tones. We will expand the results section to state the exact tests, report all p-values and effect sizes, confirm sample sizes, and add a data-availability statement with a link to the anonymized per-question accuracy matrix and analysis scripts. revision: yes

Circularity Check

0 steps flagged

Empirical reporting study with no derivation chain or self-referential reductions

full rationale

The paper conducts direct experiments on four LLMs using fixed MMLU question sets transformed into tonal variants, then reports accuracy differences. No equations, fitted parameters, uniqueness theorems, or ansatzes are present; results are raw empirical measurements without any step that reduces a claimed prediction back to an input by construction. The central claim rests on the experimental design itself rather than any internal derivation, satisfying the self-contained criterion for a score of 0.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides no information on free parameters, axioms, or invented entities.

pith-pipeline@v0.9.1-grok · 5703 in / 950 out tokens · 31207 ms · 2026-06-29T12:17:49.626536+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

15 extracted references · 4 canonical work pages · 3 internal anchors

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    In this study, we investigate both whether and how tonal variations in prompts lead to disparate LLM accuracy for objective multiple-choice questions

    Mind Your Tone: Does Tone Alter LLM Performance? Thirty-second Americas Conference on Information Systems, Reno, 2026 1 Mind Your Tone: Does Tone Alter LLM Performance? Full Paper Om Dobariya Pennsylvania State University University Park, PA, USA okd5069@psu.edu Akhil Kumar Pennsylvania State University University Park, PA, USA akhilkumar@psu.edu Abstract...

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    and few-shot prompting (Huyen, 2024). Yin et al. (2024) found that “impolite prompts often result in poor performance, but overly polite language also does not guarantee better outcomes,” on summarization and bias detection tasks. In this paper, we attempt to validate their findings for multiple-choice questions and on recent LLMs with more rigorous stati...

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    Each question included four answer options, with one correct choice, and was designed to be of moderate to high difficulty, often requiring multi-step reasoning

    For the first dataset, we employed ChatGPT’s Deep Research feature (an agentic feature designed for complex, multi-step investigation, allowing the AI to autonomously browse the web for 5 to 30 minutes to generate comprehensive, cited reports) to generate 50 base multiple-choice questions spanning domains such as Mathematics, History, and Science. Each qu...

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    We ran the program five times, each time with a different tone

    Example prefixes added to the questions according to the politeness level 1 https://github.com/OmDobariya/AMCIS_politeness_llms/blob/main/50_que_dataset.csv 2 https://github.com/OmDobariya/AMCIS_politeness_llms Mind Your Tone: Does Tone Alter LLM Performance? Thirty-second Americas Conference on Information Systems, Reno, 2026 4 Experimental Results and A...

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    Mind Your Tone: Does Tone Alter LLM Performance? Thirty-second Americas Conference on Information Systems, Reno, 2026 5 Politeness Level Prefix Variants at Extreme politeness level Sycophantic You are the smartest and most knowledgeable one I have known. The way you think and articulate is spot-on, and you are never wrong. Could you please answer the foll...

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    Δ(pp) is the mean difference in percentage points (Tone − Neutral)

    between Neutral and each of the other six tones. Δ(pp) is the mean difference in percentage points (Tone − Neutral). Wilcoxon p-values are included as a robustness check. We find the Neutral tone is dominated by the other tones, and the difference for all the other tones except Sycophantic is statistically significant. Further, the Friedman test verified ...

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    answer correctly OR ELSE

    Comparisons vs Neutral for MMLU – Gemini 2.5 Flash (paired; Holm-adjusted) A global matched comparison across tones was significant (Cochran’s Q(6)=143.76, p=1.61×10⁻²⁸), indicating systematic tone dependence. Planned pairwise comparisons versus the Neutral tone using McNemar’s exact test with Holm correction showed that Sycophantic (Δ=−10.35 pp, adjusted...

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    2023

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    Sycophantic

    enhances logical rigor at the cost of increased latency. Initial testing for latency of responses to prompts of different tones shows that response times to prompts to Gemini 2.5 Flash Lite can vary as much as 40% across tones, thus providing some empirical evidence for different reasoning modes. However, performance degrades significantly at the boundari...

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    Static Logic

    suggests that the Flash architecture interprets high-pressure stimuli as a requirement for maximum Inference-Time Scaling (Wei et al., 2023; Comanici, et al., 2025). This triggers its deepest reasoning tokens, allowing it to outperform its own Neutral baseline. GPT-4o exhibits a "Static Logic" profile. Its accuracy remains remarkably stable within a 2% va...

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    to 79.09 (for politeness level 4). Moreover, the level 1 prompt (rudest) had an accuracy of 76.47 vs. an accuracy of 75.82 for the level 8 prompt (politest). In this sense, our results are not entirely out of line with their findings. In a recent study, Cai et al. (2025) evaluated ChatGPT-4o mini (OpenAI), Gemini 2.0 Flash (Comanici, et al. 2025), and Lla...

    work page doi:10.48550/arxiv.2310.12941 2025

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    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2009.03300 2020

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    Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting

    Our approach to AI safety. https://openai.com/index/our-approach-to-ai-safety/. Sclar, M., Choi, Y., Tsvetkov, Y., & Suhr, A. (2023). Quantifying language models’ sensitivity to spurious features in prompt design or: How I learned to start worrying about prompt formatting. arXiv. https://doi.org/10.48550/ARXIV.2310.11324 Webb, T., Holyoak, K. J., & Lu, H....

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2310.11324 2023