Large Language Models Understand and Can Be Enhanced by Emotional Stimuli

"exploring the ability of LLMs to understand emotional stimuli... automatic experiments on 45 tasks using various LLMs... span deterministic and generative applications ... show that LLMs have a grasp of emotional intelligence, and their performance can be improved with ... “EmotionPrompt” ... [which] requires only the addition of emotional stimuli to the initial prompts... to design effective emotional stimuli ... we take inspiration from three types of well-established psychological phenomena":

• self-monitoring: "the process by which individuals regulate and control their behavior in response to social situations and the reactions of others"
• social cognitive theory: "stresses that learning can be closely linked to watching others in social settings, personal experiences, and exposure to information... that individuals seek to develop a sense of agency for exerting a large degree of control over important events in their lives"
• cognitive emotion regulation: "people lacking emotion regulation skills are more likely to engage in compulsive behavior and use poor coping strategies... Techniques from this theory, such as reappraisal, can help".

Some key findings - EmotionPrompt:

• improves more in few-shot setting than zero-shot
• "enhanced capacity for generating ethically responsible responses"
• "enriched supporting evidence and superior linguistic articulation"
• "stimulates the creative faculties and overarching cognizance of LLMs"
• may elicit responses with "more definitive terms, such as “completely” and “will not”, while the narrative produced by the original prompt adopts a more tempered tone"

The paper goes on to examine why this works, and which emotional stimuli are most effective.