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 1–25 of 77 papers

TitleStatusHype
RLVER: Reinforcement Learning with Verifiable Emotion Rewards for Empathetic AgentsCode3
EmoMeta: A Multimodal Dataset for Fine-grained Emotion Classification in Chinese MetaphorsCode0
Exponential Shift: Humans Adapt to AI Economies—0
Modelling Emotions in Face-to-Face Setting: The Interplay of Eye-Tracking, Personality, and Temporal Dynamics—0
SAGE: Steering and Refining Dialog Generation with State-Action AugmentationCode1
REALTALK: A 21-Day Real-World Dataset for Long-Term ConversationCode1
EmoAssist: Emotional Assistant for Visual Impairment Community—0
EmoBench-M: Benchmarking Emotional Intelligence for Multimodal Large Language Models—0
EmoXpt: Analyzing Emotional Variances in Human Comments and LLM-Generated Responses—0
Enhancing Human-Like Responses in Large Language Models—0
Sloth: scaling laws for LLM skills to predict multi-benchmark performance across familiesCode0
A Survey on Human-Centric LLMs—0
Emotion-Aware Interaction Design in Intelligent User Interface Using Multi-Modal Deep Learning—0
Generative AI and Its Impact on Personalized Intelligent Tutoring Systems—0
AER-LLM: Ambiguity-aware Emotion Recognition Leveraging Large Language Models—0
EmotionQueen: A Benchmark for Evaluating Empathy of Large Language Models—0
StressPrompt: Does Stress Impact Large Language Models and Human Performance Similarly?—0
MRAC Track 1: 2nd Workshop on Multimodal, Generative and Responsible Affective Computing—0
A Practice of Post-Training on Llama-3 70B with Optimal Selection of Additional Language Mixture Ratio—0
Enhancing AI-Driven Psychological Consultation: Layered Prompts with Large Language Models—0
Human-In-The-Loop Machine Learning for Safe and Ethical Autonomous Vehicles: Principles, Challenges, and Opportunities—0
Appraisal-Guided Proximal Policy Optimization: Modeling Psychological Disorders in Dynamic Grid World—0
Real Time Emotion Analysis Using Deep Learning for Education, Entertainment, and Beyond—0
Generative Technology for Human Emotion Recognition: A Scope Review—0
Empathic Grounding: Explorations using Multimodal Interaction and Large Language Models with Conversational AgentsCode0
Show:102550
← PrevPage 1 of 4Next →

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