Emotional Intelligence
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Emotional Intelligence (EI) is a measure of "The ability to monitor one’s own and others’ feelings, to discriminate among them, and to use this information to guide one’s thinking and action." (Salovey and Mayer, 1990). EI is further broken down into four branches: perceiving, using, understanding and managing emotions (Mayer & Salovey, 1997). Of particular relevance to language models that operate exclusively in the text modality is emotional understanding (EU). This is defined as the ability to interpret and analyse the language of emotions, to comprehend complex emotional states, and understand how these emotions can influence behaviour and decision-making.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Verifiable Unlearning on Edge
EmoMeta: A Multimodal Dataset for Fine-grained Emotion Classification in Chinese Metaphors
TeleSparse: Practical Privacy-Preserving Verification of Deep Neural Networks
Exponential Shift: Humans Adapt to AI Economies
Modelling Emotions in Face-to-Face Setting: The Interplay of Eye-Tracking, Personality, and Temporal Dynamics
SAGE: Steering and Refining Dialog Generation with State-Action Augmentation
REALTALK: A 21-Day Real-World Dataset for Long-Term Conversation
EmoAssist: Emotional Assistant for Visual Impairment Community
EmoBench-M: Benchmarking Emotional Intelligence for Multimodal Large Language Models
EmoXpt: Analyzing Emotional Variances in Human Comments and LLM-Generated Responses
Enhancing Human-Like Responses in Large Language Models
VerifBFL: Leveraging zk-SNARKs for A Verifiable Blockchained Federated Learning
Sloth: scaling laws for LLM skills to predict multi-benchmark performance across families
A Survey on Human-Centric LLMs
Emotion-Aware Interaction Design in Intelligent User Interface Using Multi-Modal Deep Learning
Generative AI and Its Impact on Personalized Intelligent Tutoring Systems
AER-LLM: Ambiguity-aware Emotion Recognition Leveraging Large Language Models
EmotionQueen: A Benchmark for Evaluating Empathy of Large Language Models
StressPrompt: Does Stress Impact Large Language Models and Human Performance Similarly?
MRAC Track 1: 2nd Workshop on Multimodal, Generative and Responsible Affective Computing
A Practice of Post-Training on Llama-3 70B with Optimal Selection of Additional Language Mixture Ratio
Enhancing AI-Driven Psychological Consultation: Layered Prompts with Large Language Models
Human-In-The-Loop Machine Learning for Safe and Ethical Autonomous Vehicles: Principles, Challenges, and Opportunities
Appraisal-Guided Proximal Policy Optimization: Modeling Psychological Disorders in Dynamic Grid World
Real Time Emotion Analysis Using Deep Learning for Education, Entertainment, and Beyond
Generative Technology for Human Emotion Recognition: A Scope Review
Empathic Grounding: Explorations using Multimodal Interaction and Large Language Models with Conversational Agents
EmoLLM: Multimodal Emotional Understanding Meets Large Language Models
Language Model Council: Democratically Benchmarking Foundation Models on Highly Subjective Tasks
Identity-free Artificial Emotional Intelligence via Micro-Gesture Understanding
Empathy Through Multimodality in Conversational Interfaces
NegativePrompt: Leveraging Psychology for Large Language Models Enhancement via Negative Emotional Stimuli
Privacy-Preserving UCB Decision Process Verification via zk-SNARKs
Research on emotionally intelligent dialogue generation based on automatic dialogue system
Emotional Intelligence Through Artificial Intelligence : NLP and Deep Learning in the Analysis of Healthcare Texts
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