  Large Language Models (LLMs) have become integral to a wide spectrum of
applications, ranging from traditional computing tasks to advanced artificial
intelligence (AI) applications. This widespread adoption has spurred extensive
research into LLMs across various disciplines, including the social sciences.
Notably, studies have revealed that LLMs possess emotional intelligence, which
can be further developed through positive emotional stimuli. This discovery
raises an intriguing question: can negative emotions similarly influence LLMs,
potentially enhancing their performance? In response to this question, we
introduce NegativePrompt, a novel approach underpinned by psychological
principles, involving ten specifically designed negative emotional stimuli. We
embark on rigorous experimental evaluations of five LLMs including
Flan-T5-Large, Vicuna, Llama 2, ChatGPT, and GPT-4, across a set of 45 tasks.
The results are revealing: NegativePrompt markedly enhances the performance of
LLMs, evidenced by relative improvements of 12.89% in Instruction Induction
tasks and 46.25% in BIG-Bench tasks. Moreover, we conduct attention
visualization experiments to decipher the underlying mechanisms of
NegativePrompt's influence. Our research contributes significantly to the
understanding of LLMs and emotion interaction, demonstrating the practical
efficacy of NegativePrompt as an emotion-driven method and offering novel
insights for the enhancement of LLMs in real-world applications. The code is
available at https://github.com/wangxu0820/NegativePrompt.
