Large language models (LLMs) have achieved significant performance in many fields such as reasoning, language understanding, and math problem-solving, and are regarded as a crucial step to artificial general intelligence (AGI). However, the sensitivity of LLMs to prompts remains a major bottleneck for their daily adoption. In this paper, we take inspiration from psychology and propose EmotionPrompt to explore emotional intelligence to enhance the performance of LLMs. EmotionPrompt operates on a remarkably straightforward principle: the incorporation of emotional stimulus into prompts. Experimental results demonstrate that our EmotionPrompt, using the same single prompt templates, significantly outperforms original zero-shot prompt and Zero-shot-CoT on 8 tasks with diverse models: ChatGPT, Vicuna-13b, Bloom, and T5. Further, EmotionPrompt was observed to improve both truthfulness and informativeness. We believe that EmotionPrompt heralds a novel avenue for exploring interdisciplinary knowledge for humans-LLMs interaction.

翻译：大语言模型在推理、语言理解、数学问题求解等多个领域取得了显著性能，被视为迈向通用人工智能的关键一步。然而，大语言模型对提示词的敏感性仍是其日常应用的主要瓶颈。受心理学启发，本文提出EmotionPrompt，旨在通过探索情感智能来增强大语言模型的性能。EmotionPrompt的核心原则极为简洁：将情感刺激融入提示词中。实验结果表明，采用相同的单一提示模板，我们的EmotionPrompt在ChatGPT、Vicuna-13b、Bloom和T5等不同模型的8项任务上，显著优于原始的零样本提示和零样本思维链方法。此外，还观察到EmotionPrompt能够同时提升输出的真实性和信息丰富度。我们相信，EmotionPrompt为探索人类与大语言模型交互的跨学科知识开辟了一条新途径。