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Emotional intelligence significantly impacts our daily behaviors andinteractions. Although Large Language Models (LLMs) are increasingly viewed asa stride toward artificial general intelligence, exhibiting impressiveperformance in numerous tasks, it is still uncertain if LLMs can genuinelygrasp psychological emotional stimuli. Understanding and responding toemotional cues gives humans a distinct advantage in problem-solving. In thispaper, we take the first step towards exploring the ability of LLMs tounderstand emotional stimuli. To this end, we first conduct automaticexperiments on 45 tasks using various LLMs, including Flan-T5-Large, Vicuna,Llama 2, BLOOM, ChatGPT, and GPT-4. Our tasks span deterministic and generativeapplications that represent comprehensive evaluation scenarios. Our automaticexperiments show that LLMs have a grasp of emotional intelligence, and theirperformance can be improved with emotional prompts (which we call"EmotionPrompt" that combines the original prompt with emotional stimuli),e.g., 8.00% relative performance improvement in Instruction Induction and 115%in BIG-Bench. In addition to those deterministic tasks that can beautomatically evaluated using existing metrics, we conducted a human study with106 participants to assess the quality of generative tasks using both vanillaand emotional prompts. Our human study results demonstrate that EmotionPromptsignificantly boosts the performance of generative tasks (10.9% averageimprovement in terms of performance, truthfulness, and responsibility metrics).We provide an in-depth discussion regarding why EmotionPrompt works for LLMsand the factors that may influence its performance. We posit that EmotionPromptheralds a novel avenue for exploring interdisciplinary knowledge for human-LLMsinteraction.

Large Language Models Understand and Can be Enhanced by Emotional Stimuli