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Found 16 papers, 2 papers with code
GPT-4 Emulates Average-Human Emotional Cognition from a Third-Person Perspective
This suggests LLMs agree with humans' attributions of others' emotions in stereotypical scenarios remarkably more than self-attributions of emotions in idiosyncratic situations.
In-Depth Analysis of Emotion Recognition through Knowledge-Based Large Language Models
Emotion recognition in social situations is a complex task that requires integrating information from both facial expressions and the situational context.
Are LLMs Effective Negotiators? Systematic Evaluation of the Multifaceted Capabilities of LLMs in Negotiation Dialogues
A successful negotiation demands a deep comprehension of the conversation context, Theory-of-Mind (ToM) skills to infer the partner's motives, as well as strategic reasoning and effective communication, making it challenging for automated systems.
Can Language Model Moderators Improve the Health of Online Discourse?
Conversational moderation of online communities is crucial to maintaining civility for a constructive environment, but it is challenging to scale and harmful to moderators.
Context Unlocks Emotions: Text-based Emotion Classification Dataset Auditing with Large Language Models
In this work, we propose a formal definition of textual context to motivate a prompting strategy to enhance such contextual information.
Be Selfish, But Wisely: Investigating the Impact of Agent Personality in Mixed-Motive Human-Agent Interactions
A natural way to design a negotiation dialogue system is via self-play RL: train an agent that learns to maximize its performance by interacting with a simulated user that has been designed to imitate human-human dialogue data.
Is GPT a Computational Model of Emotion? Detailed Analysis
This paper investigates the emotional reasoning abilities of the GPT family of large language models via a component perspective.
Social Influence Dialogue Systems: A Survey of Datasets and Models For Social Influence Tasks
Dialogue systems capable of social influence such as persuasion, negotiation, and therapy, are essential for extending the use of technology to numerous realistic scenarios.
Opponent Modeling in Negotiation Dialogues by Related Data Adaptation
A practical model for this task needs to infer these priorities of the opponent on the fly based on partial dialogues as input, without needing additional annotations for training.
Understanding of Emotion Perception from Art
Computational modeling of the emotions evoked by art in humans is a challenging problem because of the subjective and nuanced nature of art and affective signals.
Towards Emotion-Aware Agents For Negotiation Dialogues
Negotiation is a complex social interaction that encapsulates emotional encounters in human decision-making.
CaSiNo: A Corpus of Campsite Negotiation Dialogues for Automatic Negotiation Systems
Automated systems that negotiate with humans have broad applications in pedagogy and conversational AI.
Exploring Early Prediction of Buyer-Seller Negotiation Outcomes
Agents that negotiate with humans find broad applications in pedagogy and conversational AI.
AVEC 2016 - Depression, Mood, and Emotion Recognition Workshop and Challenge
The Audio/Visual Emotion Challenge and Workshop (AVEC 2016) "Depression, Mood and Emotion" will be the sixth competition event aimed at comparison of multimedia processing and machine learning methods for automatic audio, visual and physiological depression and emotion analysis, with all participants competing under strictly the same conditions.
The Distress Analysis Interview Corpus of human and computer interviews
The Distress Analysis Interview Corpus (DAIC) contains clinical interviews designed to support the diagnosis of psychological distress conditions such as anxiety, depression, and post traumatic stress disorder.
