EmoLLM: Appraisal-Grounded Cognitive-Emotional Co-Reasoning in Large Language Models

2026-03-17

AI Summary

This work addresses the challenge that current large language models struggle to simultaneously ensure factual accuracy and emotional appropriateness in dialogue. The authors propose an explicit Appraisal-based Reasoning Graph (ARG), grounded in appraisal theory, which unifies contextual facts, user needs, appraisal dimensions, emotional states, and response strategies into a coherent framework for joint cognitive and affective reasoning. Innovatively, appraisal theory is formalized into a computable structure and integrated with multi-turn role-playing reinforcement learning and a reverse-perspective reward mechanism to optimize response generation. Experimental results demonstrate that the proposed approach significantly enhances response quality and user emotional experience across diverse conversational scenarios while maintaining high factual reliability.

Technology Category

• Knowledge Representation and Reasoning: Argumentation
• Natural Language Processing: (Large) Language Models
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Large language models (LLMs) demonstrate strong cognitive intelligence (IQ), yet many real-world interactions also require emotional intelligence (EQ) to produce responses that are both factually reliable and emotionally appropriate. In settings such as emotional support, technical assistance, and consultation, effective dialogue depends on how situations are appraised with respect to the user's needs, goals, and coping capacity. Inspired by appraisal theory, we propose EmoLLM, an appraisal-grounded framework for IQ/EQ co-reasoning in dialogue. EmoLLM uses an explicit Appraisal Reasoning Graph (ARG) to structure intermediate reasoning over contextual facts, inferred user needs, appraisal dimensions, emotional states, and response strategies before generating a reply. We train EmoLLM in a multi-turn role-play environment with reinforcement learning, where reverse-perspective reasoning provides reward signals based on predicted user-side consequences of responses. Across diverse dialogue settings, EmoLLM improves emotional state outcomes and response quality over strong baselines while preserving strong factual reliability.

Problem

• emotional intelligence
• cognitive-emotional co-reasoning
• appraisal theory
• large language models
• dialogue systems

Innovation

• Appraisal Theory
• Cognitive-Emotional Co-Reasoning
• Appraisal Reasoning Graph
• Reverse-Perspective Reinforcement Learning
• Emotionally Intelligent LLMs