The Good, The Bad, and Why: Unveiling Emotions in Generative AI

• URL: http://arxiv.org/abs/2312.11111v3
• Date: Fri, 7 Jun 2024 09:25:31 GMT
• Title: The Good, The Bad, and Why: Unveiling Emotions in Generative AI
• Authors: Cheng Li, Jindong Wang, Yixuan Zhang, Kaijie Zhu, Xinyi Wang, Wenxin Hou, Jianxun Lian, Fang Luo, Qiang Yang, Xing Xie,
• Abstract summary: We show that EmotionPrompt can boost the performance of AI models while EmotionAttack can hinder it. EmotionDecode reveals that AI models can comprehend emotional stimuli akin to the mechanism of dopamine in the human brain.
• Score: 73.94035652867618
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Emotion significantly impacts our daily behaviors and interactions. While recent generative AI models, such as large language models, have shown impressive performance in various tasks, it remains unclear whether they truly comprehend emotions. This paper aims to address this gap by incorporating psychological theories to gain a holistic understanding of emotions in generative AI models. Specifically, we propose three approaches: 1) EmotionPrompt to enhance AI model performance, 2) EmotionAttack to impair AI model performance, and 3) EmotionDecode to explain the effects of emotional stimuli, both benign and malignant. Through extensive experiments involving language and multi-modal models on semantic understanding, logical reasoning, and generation tasks, we demonstrate that both textual and visual EmotionPrompt can boost the performance of AI models while EmotionAttack can hinder it. Additionally, EmotionDecode reveals that AI models can comprehend emotional stimuli akin to the mechanism of dopamine in the human brain. Our work heralds a novel avenue for exploring psychology to enhance our understanding of generative AI models.

Related papers

• MEMO-Bench: A Multiple Benchmark for Text-to-Image and Multimodal Large Language Models on Human Emotion Analysis [53.012111671763776]
  This study introduces MEMO-Bench, a comprehensive benchmark consisting of 7,145 portraits, each depicting one of six different emotions. Results demonstrate that existing T2I models are more effective at generating positive emotions than negative ones. Although MLLMs show a certain degree of effectiveness in distinguishing and recognizing human emotions, they fall short of human-level accuracy.
  arXiv  Detail & Related papers  (2024-11-18T02:09:48Z)
• Emotional Images: Assessing Emotions in Images and Potential Biases in Generative Models [0.0]
  This paper examines potential biases and inconsistencies in emotional evocation of images produced by generative artificial intelligence (AI) models. We compare the emotions evoked by an AI-produced image to the emotions evoked by prompts used to create those images. Findings indicate that AI-generated images frequently lean toward negative emotional content, regardless of the original prompt.
  arXiv  Detail & Related papers  (2024-11-08T21:42:50Z)
• 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)
• Think out Loud: Emotion Deducing Explanation in Dialogues [57.90554323226896]
  We propose a new task "Emotion Deducing Explanation in Dialogues" (EDEN) EDEN recognizes emotion and causes in an explicitly thinking way. It can help Large Language Models (LLMs) achieve better recognition of emotions and causes.
  arXiv  Detail & Related papers  (2024-06-07T08:58:29Z)
• Improved Emotional Alignment of AI and Humans: Human Ratings of Emotions Expressed by Stable Diffusion v1, DALL-E 2, and DALL-E 3 [10.76478480925475]
  Generative AI systems are increasingly capable of expressing emotions via text and imagery. We measure the alignment between emotions expressed by generative AI and human perceptions. We show that the alignment significantly depends upon the AI model used and the emotion itself.
  arXiv  Detail & Related papers  (2024-05-28T18:26:57Z)
• ECR-Chain: Advancing Generative Language Models to Better Emotion-Cause Reasoners through Reasoning Chains [61.50113532215864]
  Causal Emotion Entailment (CEE) aims to identify the causal utterances in a conversation that stimulate the emotions expressed in a target utterance. Current works in CEE mainly focus on modeling semantic and emotional interactions in conversations. We introduce a step-by-step reasoning method, Emotion-Cause Reasoning Chain (ECR-Chain), to infer the stimulus from the target emotional expressions in conversations.
  arXiv  Detail & Related papers  (2024-05-17T15:45:08Z)
• A Portrait of Emotion: Empowering Self-Expression through AI-Generated Art [0.0]
  We investigated the potential and limitations of generative artificial intelligence (AI) in reflecting the authors' cognitive processes through creative expression. Results show a preference for images based on the descriptions of the authors' emotions over the main events. Our research framework with generative AIs can help design AI-based interventions in related fields.
  arXiv  Detail & Related papers  (2023-04-26T06:54:53Z)
• HICEM: A High-Coverage Emotion Model for Artificial Emotional Intelligence [9.153146173929935]
  Next-generation artificial emotional intelligence (AEI) is taking center stage to address users' desire for deeper, more meaningful human-machine interaction. Unlike theory of emotion, which has been the historical focus in psychology, emotion models are a descriptive tools. This work has broad implications in social robotics, human-machine interaction, mental healthcare, and computational psychology.
  arXiv  Detail & Related papers  (2022-06-15T15:21:30Z)
• Enhancing Cognitive Models of Emotions with Representation Learning [58.2386408470585]
  We present a novel deep learning-based framework to generate embedding representations of fine-grained emotions. Our framework integrates a contextualized embedding encoder with a multi-head probing model. Our model is evaluated on the Empathetic Dialogue dataset and shows the state-of-the-art result for classifying 32 emotions.
  arXiv  Detail & Related papers  (2021-04-20T16:55:15Z)
• Modeling emotion for human-like behavior in future intelligent robots [0.913755431537592]
  We show how neuroscience can help advance the current state of the art. We argue that a stronger integration of emotion-related processes in robot models is critical for the design of human-like behavior.
  arXiv  Detail & Related papers  (2020-09-30T17:32:30Z)

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.