Emotional Intelligence
8 papers with code • 1 benchmarks • 2 datasets
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.
Benchmarks
These leaderboards are used to track progress in Emotional Intelligence
| Trend | Dataset | Best Model | Paper | Code | Compare |
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Emotional Intelligence
Cards Against Humanity
Most implemented papers
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world.
Are there intelligent Turing machines?
This paper introduces a new computing model based on the cooperation among Turing machines called orchestrated machines.
Real-Time Emotion Classification Using EEG Data Stream in E-Learning Contexts
The proposed RECS is capable of classifying emotions in real-time by training the model in an online fashion using an EEG signal stream.
Emotion Twenty Questions Dialog System for Lexical Emotional Intelligence
This paper presents a web-based demonstration of Emotion Twenty Questions (EMO20Q), a dialog game whose purpose is to study how people describe emotions.
From Multilingual Complexity to Emotional Clarity: Leveraging Commonsense to Unveil Emotions in Code-Mixed Dialogues
Recognizing that emotional intelligence encompasses a comprehension of worldly knowledge, we propose an innovative approach that integrates commonsense information with dialogue context to facilitate a deeper understanding of emotions.
Divergences between Language Models and Human Brains
In this work, we systematically explore the divergences between human and machine language processing by examining the differences between LM representations and human brain responses to language as measured by Magnetoencephalography (MEG) across two datasets in which subjects read and listened to narrative stories.
EQ-Bench: An Emotional Intelligence Benchmark for Large Language Models
We introduce EQ-Bench, a novel benchmark designed to evaluate aspects of emotional intelligence in Large Language Models (LLMs).
EmoBench: Evaluating the Emotional Intelligence of Large Language Models
Recent advances in Large Language Models (LLMs) have highlighted the need for robust, comprehensive, and challenging benchmarks.
