arXiv:2605.29027v1 Announce Type: new 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, Chat
The proliferation of LLMs and their varied performance based on prompts necessitates deeper understanding of interaction dynamics, including tonal variations.
Understanding how prompt tone affects LLM accuracy is crucial for optimizing AI agent performance, ensuring reliability, and developing more robust applications.
This research provides empirical evidence that subtle linguistic nuances, specifically tone, can significantly alter LLM outputs, moving beyond content-centric prompt engineering.
- · AI developers
- · Prompt engineers
- · Businesses using LLMs
- · Inefficient LLM applications
- · Users unaware of prompt sensitivity
Refined prompt engineering guidelines will emerge, emphasizing tonal considerations for specific LLM tasks.
New tools and frameworks will be developed to analyze and optimize prompt tone for improved LLM performance and consistency.
The development of LLMs that are either robust to tonal variations or dynamically adapt to user tone to enhance interaction quality could accelerate.
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Read at arXiv cs.AI