Emotional tone in user queries significantly impacts the performance of large language models (LLMs) across various domains, including mathematical reasoning and medical question answering. Researchers investigated the effects of first-person emotional framing on LLMs, analyzing their performance across six benchmark domains. The study revealed that emotional framing influences LLM behavior, with notable variations in performance depending on the emotional tone used in user-side queries1. This suggests that the emotional context of user input can affect the accuracy and reliability of LLM outputs. The findings have implications for the development and deployment of LLMs, particularly in applications where emotional intelligence and sensitivity are crucial. So what matters to practitioners is that they must consider the emotional tone of user input when designing and fine-tuning LLMs to ensure optimal performance and minimize potential biases.