Abstract
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 this https URL.
Abstract (translated)
大语言模型（LLMs）已经成为各种应用领域的不可或缺的一部分，从传统计算任务到高级人工智能（AI）应用。这种广泛的应用催生了在各个学科领域对LLMs的深入研究。值得注意的是，研究发现LLMs具有情感智能，可以通过正向情感刺激进一步发展。这一发现引发了一个引人入胜的问题：负向情感是否也会影响LLMs，从而可能增强其性能？为了回答这个问题，我们引入了NegativePrompt，一种基于心理原则的新型方法，包括十种专门设计的负情感刺激。我们进行了对包括Flan-T5-Large、Vicuna、Llama 2、ChatGPT和GPT-4在内的五种LLM的严格实验评估，涉及45个任务。结果表明，NegativePrompt显著增强了LLM的性能，表现为在指导诱导任务中的相对改善率为12.89%，在BIG-Bench任务中的相对改善率为46.25%。此外，我们进行了注意图实验，以揭示NegativePrompt影响背后的机制。我们的研究对LLMs和情感交互的理解做出了重要贡献，证明了NegativePrompt作为一种情感驱动的方法在实际应用中的实际有效性，并为提高LLM在现实应用中的性能提供了新颖的见解。代码可在此处访问：https://www.xxx.com/
URL
https://arxiv.org/abs/2405.02814
