NegativePrompt: Leveraging Psychology for Large Language Models Enhancement via Negative Emotional Stimuli

• URL: http://arxiv.org/abs/2405.02814v2
• Date: Sun, 12 May 2024 16:11:48 GMT
• Title: NegativePrompt: Leveraging Psychology for Large Language Models Enhancement via Negative Emotional Stimuli
• Authors: Xu Wang, Cheng Li, Yi Chang, Jindong Wang, Yuan Wu,
• Abstract summary: Large Language Models (LLMs) have become integral to a wide spectrum of applications. LLMs possess emotional intelligence, which can be further developed through positive emotional stimuli. We introduce NegativePrompt, a novel approach underpinned by psychological principles.
• Score: 21.846500669385193
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Retrieving Implicit and Explicit Emotional Events Using Large Language Models [4.245183693179267]
  Large language models (LLMs) have garnered significant attention in recent years due to their impressive performance. This study investigates LLMs' emotion retrieval capabilities in commonsense.
  arXiv  Detail & Related papers  (2024-10-24T19:56:28Z)
• AER-LLM: Ambiguity-aware Emotion Recognition Leveraging Large Language Models [18.482881562645264]
  This study is the first to explore the potential of Large Language Models (LLMs) in recognizing ambiguous emotions. We design zero-shot and few-shot prompting and incorporate past dialogue as context information for ambiguous emotion recognition.
  arXiv  Detail & Related papers  (2024-09-26T23:25:21Z)
• Do Large Language Models Possess Sensitive to Sentiment? [18.88126980975737]
  Large Language Models (LLMs) have recently displayed their extraordinary capabilities in language understanding. This paper investigates the ability of LLMs to detect and react to sentiment in text modal.
  arXiv  Detail & Related papers  (2024-09-04T01:40:20Z)
• EmoLLM: Multimodal Emotional Understanding Meets Large Language Models [61.179731667080326]
  Multi-modal large language models (MLLMs) have achieved remarkable performance on objective multimodal perception tasks. But their ability to interpret subjective, emotionally nuanced multimodal content remains largely unexplored. EmoLLM is a novel model for multimodal emotional understanding, incorporating with two core techniques.
  arXiv  Detail & Related papers  (2024-06-24T08:33:02Z)
• The Strong Pull of Prior Knowledge in Large Language Models and Its Impact on Emotion Recognition [74.04775677110179]
  In-context Learning (ICL) has emerged as a powerful paradigm for performing natural language tasks with Large Language Models (LLM) We show that LLMs have strong yet inconsistent priors in emotion recognition that ossify their predictions. Our results suggest that caution is needed when using ICL with larger LLMs for affect-centered tasks outside their pre-training domain.
  arXiv  Detail & Related papers  (2024-03-25T19:07:32Z)
• GPT as Psychologist? Preliminary Evaluations for GPT-4V on Visual Affective Computing [74.68232970965595]
  Multimodal large language models (MLLMs) are designed to process and integrate information from multiple sources, such as text, speech, images, and videos. This paper assesses the application of MLLMs with 5 crucial abilities for affective computing, spanning from visual affective tasks and reasoning tasks.
  arXiv  Detail & Related papers  (2024-03-09T13:56:25Z)
• Enhancing Emotional Generation Capability of Large Language Models via Emotional Chain-of-Thought [50.13429055093534]
  Large Language Models (LLMs) have shown remarkable performance in various emotion recognition tasks. We propose the Emotional Chain-of-Thought (ECoT) to enhance the performance of LLMs on various emotional generation tasks.
  arXiv  Detail & Related papers  (2024-01-12T16:42:10Z)
• Emotional Intelligence of Large Language Models [9.834823298632374]
  Large Language Models (LLMs) have demonstrated remarkable abilities across numerous disciplines. However, their alignment with human emotions and values, which is critical for real-world applications, has not been systematically evaluated. Here, we assessed LLMs' Emotional Intelligence (EI), encompassing emotion recognition, interpretation, and understanding.
  arXiv  Detail & Related papers  (2023-07-18T07:49:38Z)
• Large Language Models Understand and Can be Enhanced by Emotional Stimuli [53.53886609012119]
  We take the first step towards exploring the ability of Large Language Models to understand emotional stimuli. Our experiments show that LLMs have a grasp of emotional intelligence, and their performance can be improved with emotional prompts. Our human study results demonstrate that EmotionPrompt significantly boosts the performance of generative tasks.
  arXiv  Detail & Related papers  (2023-07-14T00:57:12Z)
• Clever Hans or Neural Theory of Mind? Stress Testing Social Reasoning in Large Language Models [82.50173296858377]
  Many anecdotal examples were used to suggest newer large language models (LLMs) like ChatGPT and GPT-4 exhibit Neural Theory-of-Mind (N-ToM) We investigate the extent of LLMs' N-ToM through an extensive evaluation on 6 tasks and find that while LLMs exhibit certain N-ToM abilities, this behavior is far from being robust.
  arXiv  Detail & Related papers  (2023-05-24T06:14:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.