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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 51–75 of 77 papers

TitleStatusHype
StressPrompt: Does Stress Impact Large Language Models and Human Performance Similarly?—0
Synthesizing Affective Neurophysiological Signals Using Generative Models: A Review Paper—0
The Effect of Implicit Attitude on Self-Concept, Emotional Intelligence, Personality Characteristics and Ego Defense Styles—0
The neural signature of inner peace: morphometric differences between high and low accepters—0
The Nexus between Job Burnout and Emotional Intelligence on Turnover Intention in Oil and Gas Companies in the UAE—0
The SPACE THEA Project—0
Unlocking the Emotional World of Visual Media: An Overview of the Science, Research, and Impact of Understanding Emotion—0
Learning Transferable Features for Speech Emotion Recognition—0
Lie-Sensor: A Live Emotion Verifier or a Licensor for Chat Applications using Emotional Intelligence—0
LLaVA-Docent: Instruction Tuning with Multimodal Large Language Model to Support Art Appreciation Education—0
Machine Learning Algorithms for Depression Detection and Their Comparison—0
MMTF-DES: A Fusion of Multimodal Transformer Models for Desire, Emotion, and Sentiment Analysis of Social Media Data—0
Modelling Emotions in Face-to-Face Setting: The Interplay of Eye-Tracking, Personality, and Temporal Dynamics—0
MRAC Track 1: 2nd Workshop on Multimodal, Generative and Responsible Affective Computing—0
Multimodal Emotion Recognition using Transfer Learning from Speaker Recognition and BERT-based models—0
occ2vec: A principal approach to representing occupations using natural language processing—0
Future Trends for Human-AI Collaboration: A Comprehensive Taxonomy of AI/AGI Using Multiple Intelligences and Learning Styles—0
Real Time Emotion Analysis Using Deep Learning for Education, Entertainment, and Beyond—0
Investigating Emotion-Color Association in Deep Neural NetworksCode0
From Multilingual Complexity to Emotional Clarity: Leveraging Commonsense to Unveil Emotions in Code-Mixed DialoguesCode0
Are there intelligent Turing machines?Code0
Empathic Grounding: Explorations using Multimodal Interaction and Large Language Models with Conversational AgentsCode0
Sloth: scaling laws for LLM skills to predict multi-benchmark performance across familiesCode0
Divergences between Language Models and Human BrainsCode0
Emotion Twenty Questions Dialog System for Lexical Emotional IntelligenceCode0
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Benchmark Results

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