Mind Your Tone: Does Tone Alter LLM Performance?
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Abstract (arXiv)
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.
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References · 8
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
- 2023Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formattingcited by 778
- 2022Large Language Models Struggle to Learn Long-Tail Knowledgecited by 452
- 2022Emergent Analogical Reasoning in Large Language Modelscited by 239
- 2023Simple synthetic data reduces sycophancy in large language modelscited by 174
- 2024Should We Respect LLMs? A Cross-Lingual Study on the Influence of Prompt Politeness on LLM Performancecited by 41
- 2023Data Similarity is Not Enough to Explain Language Model Performancecited by 9
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