SOTAVerified

Emotional Intelligence

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.

Papers

Showing 2650 of 77 papers

TitleStatusHype
Empirical Interpretation of the Relationship Between Speech Acoustic Context and Emotion Recognition0
Enhancing AI-Driven Psychological Consultation: Layered Prompts with Large Language Models0
Enhancing Human-Like Responses in Large Language Models0
Enhancing Emotional Generation Capability of Large Language Models via Emotional Chain-of-Thought0
Exponential Shift: Humans Adapt to AI Economies0
Fuzzy Approach for Audio-Video Emotion Recognition in Computer Games for Children0
Generative AI and Its Impact on Personalized Intelligent Tutoring Systems0
Generative Technology for Human Emotion Recognition: A Scope Review0
HICEM: A High-Coverage Emotion Model for Artificial Emotional Intelligence0
Human-In-The-Loop Machine Learning for Safe and Ethical Autonomous Vehicles: Principles, Challenges, and Opportunities0
Identity-free Artificial Emotional Intelligence via Micro-Gesture Understanding0
Investigating Audio, Video, and Text Fusion Methods for End-to-End Automatic Personality Prediction0
Deep Multi-Facial patches Aggregation Network for Expression Classification from Face Images0
Learning Transferable Features for Speech Emotion Recognition0
Lie-Sensor: A Live Emotion Verifier or a Licensor for Chat Applications using Emotional Intelligence0
LLaVA-Docent: Instruction Tuning with Multimodal Large Language Model to Support Art Appreciation Education0
Machine Learning Algorithms for Depression Detection and Their Comparison0
MMTF-DES: A Fusion of Multimodal Transformer Models for Desire, Emotion, and Sentiment Analysis of Social Media Data0
Modelling Emotions in Face-to-Face Setting: The Interplay of Eye-Tracking, Personality, and Temporal Dynamics0
MRAC Track 1: 2nd Workshop on Multimodal, Generative and Responsible Affective Computing0
Multimodal Emotion Recognition using Transfer Learning from Speaker Recognition and BERT-based models0
occ2vec: A principal approach to representing occupations using natural language processing0
Future Trends for Human-AI Collaboration: A Comprehensive Taxonomy of AI/AGI Using Multiple Intelligences and Learning Styles0
Real Time Emotion Analysis Using Deep Learning for Education, Entertainment, and Beyond0
Reevaluating Data Partitioning for Emotion Detection in EmoWOZ0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1OpenAI gpt-4-0613EQ-Bench Score62.52Unverified
2migtissera/SynthIA-70B-v1.5EQ-Bench Score54.83Unverified
3OpenAI gpt-4-0314EQ-Bench Score53.39Unverified
4Qwen/Qwen-72B-ChatEQ-Bench Score52.44Unverified
5Anthropic Claude2EQ-Bench Score52.14Unverified
6meta-llama/Llama-2-70b-chat-hfEQ-Bench Score51.56Unverified
701-ai/Yi-34B-ChatEQ-Bench Score51.03Unverified
8OpenAI gpt-3.5-0613EQ-Bench Score49.17Unverified
9OpenAI gpt-3.5-turbo-0301EQ-Bench Score47.61Unverified
10Open-Orca/Mistral-7B-OpenOrcaEQ-Bench Score44.4Unverified