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Papers

arxiv:2509.07403

LongEmotion: Measuring Emotional Intelligence of Large Language Models in Long-Context Interaction

Published on Sep 9, 2025

· Submitted by

Hui Shen on Sep 16, 2025

#2 Paper of the day

LongEmotion

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Authors:

Weichu Liu ,

Jing Xiong ,

Chaofan Tao ,

Hui Shen ,

Abstract

LongEmotion benchmark enhances large language models' emotional intelligence in long-context scenarios using Retrieval-Augmented Generation and Collaborative Emotional Modeling.

AI-generated summary

Large language models (LLMs) make significant progress in Emotional Intelligence (EI) and long-context understanding. However, existing benchmarks tend to overlook certain aspects of EI in long-context scenarios, especially under realistic, practical settings where interactions are lengthy, diverse, and often noisy. To move towards such realistic settings, we present LongEmotion, a benchmark specifically designed for long-context EI tasks. It covers a diverse set of tasks, including Emotion Classification, Emotion Detection, Emotion QA, Emotion Conversation, Emotion Summary, and Emotion Expression. On average, the input length for these tasks reaches 8,777 tokens, with long-form generation required for Emotion Expression. To enhance performance under realistic constraints, we incorporate Retrieval-Augmented Generation (RAG) and Collaborative Emotional Modeling (CoEM), and compare them with standard prompt-based methods. Unlike conventional approaches, our RAG method leverages both the conversation context and the large language model itself as retrieval sources, avoiding reliance on external knowledge bases. The CoEM method further improves performance by decomposing the task into five stages, integrating both retrieval augmentation and limited knowledge injection. Experimental results show that both RAG and CoEM consistently enhance EI-related performance across most long-context tasks, advancing LLMs toward more practical and real-world EI applications. Furthermore, we conducted a comparative case study experiment on the GPT series to demonstrate the differences among various models in terms of EI. Code is available on GitHub at https://github.com/LongEmotion/LongEmotion, and the project page can be found at https://longemotion.github.io/.

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Paper author Paper submitter Sep 16, 2025

LongEmotion benchmark enhances large language models' emotional intelligence in long-context scenarios using Retrieval-Augmented Generation and Collaborative Emotional Modeling.