Curated Resource ( ? )

Large Language Models Understand and Can Be Enhanced by Emotional Stimuli

my notes ( ? )

"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.

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See also: Digital Transformation , Innovation Strategy , Psychology , Science&Technology , Large language models

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