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
Language Model Council: Democratically Benchmarking Foundation Models on Highly Subjective TasksCode3
EmoBench: Evaluating the Emotional Intelligence of Large Language ModelsCode2
EQ-Bench: An Emotional Intelligence Benchmark for Large Language ModelsCode2
Scaling Language Models: Methods, Analysis & Insights from Training GopherCode2
EmoLLM: Multimodal Emotional Understanding Meets Large Language ModelsCode1
Real-Time Emotion Classification Using EEG Data Stream in E-Learning ContextsCode1
NegativePrompt: Leveraging Psychology for Large Language Models Enhancement via Negative Emotional StimuliCode1
REALTALK: A 21-Day Real-World Dataset for Long-Term ConversationCode1
SAGE: Steering and Refining Dialog Generation with State-Action AugmentationCode1
Deep Multi-Facial patches Aggregation Network for Expression Classification from Face Images—0
Controllability Analysis of Functional Brain Networks—0
CuentosIE: can a chatbot about "tales with a message" help to teach emotional intelligence?—0
Affective Image Content Analysis: Two Decades Review and New Perspectives—0
Continuing Pre-trained Model with Multiple Training Strategies for Emotional Classification—0
A Practice of Post-Training on Llama-3 70B with Optimal Selection of Additional Language Mixture Ratio—0
Emotion Recognition from Multiple Modalities: Fundamentals and Methodologies—0
Challenges of Artificial Intelligence -- From Machine Learning and Computer Vision to Emotional Intelligence—0
Can Large Language Models be Good Emotional Supporter? Mitigating Preference Bias on Emotional Support Conversation—0
Appraisal-Guided Proximal Policy Optimization: Modeling Psychological Disorders in Dynamic Grid World—0
Emotional Intelligence of Large Language Models—0
Emotional Intelligence Through Artificial Intelligence : NLP and Deep Learning in the Analysis of Healthcare Texts—0
Emotion-Aware Interaction Design in Intelligent User Interface Using Multi-Modal Deep Learning—0
Large Language Models Understand and Can be Enhanced by Emotional Stimuli—0
EmotionQueen: A Benchmark for Evaluating Empathy of Large Language Models—0
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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