Mind Your Tone: Does Tone Alter LLM Performance?

Computer Science > Artificial Intelligence [Submitted on 27 May 2026] Mind Your Tone: Does Tone Alter LLM Performance? Om Dobariya, Akhil Kumar 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. Comments: 10 pages, 6 tables, 1 figure. Accepted as a full paper at the Thirty-second Americas Conference on Information Systems (AMCIS 2026), Reno. Follow-up to arXiv:2510.04950 Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC) Cite as: arXiv:2605.29027 [cs.AI]   (or arXiv:2605.29027v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2605.29027 Focus to learn more Submission history From: Om Dobariya [view email] [v1] Wed, 27 May 2026 19:23:46 UTC (698 KB) Access Paper: view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-05 Change to browse by: cs cs.CL cs.HC References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)