{"type":"ScholarlyArticle","title":"NegativePrompt: Leveraging Psychology for Large Language Models Enhancement via Negative Emotional Stimuli","authors":[{"name":"Xu Wang","slug":"xu-wang","given":"Xu","family":"Wang","affiliation":"School of Artificial Intelligence , Jilin University"},{"name":"Cheng Li","slug":"cheng-li","given":"Cheng","family":"Li","affiliation":"Institute of Software , CAS; Microsoft Research Asia"},{"name":"Yi Chang","slug":"yi-chang","given":"Yi","family":"Chang","affiliation":"School of Artificial Intelligence , Jilin University; Key Laboratory of Symbolic Computation and Knowledge Engineering , Jilin University; International Center of Future Science , Jilin University"},{"name":"Jindong Wang","slug":"jindong-wang","given":"Jindong","family":"Wang","affiliation":"Microsoft Research Asia"},{"name":"Yuan Wu","slug":"yuan-wu","given":"Yuan","family":"Wu","affiliation":"School of Artificial Intelligence , Jilin University; Key Laboratory of Symbolic Computation and Knowledge Engineering , Jilin University"}],"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 https://github.com/wangxu0820/NegativePrompt.","doi":"10.24963/ijcai.2024/719","url":"https://ijcai.org/proceedings/2024/719","pdf":"https://ijcai.org/proceedings/2024/0719.pdf","markdown":"https://ijcai.org/proceedings/2024/719.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/719","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Natural Language Processing","track":"Main Track","number":719,"pages":{"first":6504,"last":6512},"keywords":["Natural Language Processing: NLP: Applications","Natural Language Processing: NLP: Language models"],"videos":[],"references":[{"position":1,"raw":"[Ackerman, 2021] CE Ackerman. 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