LongEmotion: Measuring Emotional Intelligence of Large Language Models
  in Long-Context Interaction
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/.
68c398f9fc1747b912403aa3
Weichu Liu
claimed_verified
2025-09-18T13:27:53.079Z
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/avatars/ed25d0a3d816dff4e86e7c59989f7277.svg
Matcha
user
MatchaLwc
68c398f9fc1747b912403aa4
Jing Xiong
claimed_verified
2025-09-12T16:07:35.882Z
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/avatars/d385fcc513acef80a3b711aa92d898e5.svg
Jing Xiong
user
menik1126
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Yuxuan Hu
68c398f9fc1747b912403aa6
Zixuan Li
68c398f9fc1747b912403aa7
Minghuan Tan
68c398f9fc1747b912403aa8
Ningning Mao
68c398f9fc1747b912403aa9
Chenyang Zhao
68c398f9fc1747b912403aaa
Zhongwei Wan
68c398f9fc1747b912403aab
Chaofan Tao
68c398f9fc1747b912403aac
Wendong Xu
68c398f9fc1747b912403aad
Hui Shen
claimed_verified
2025-09-15T15:08:38.540Z
640feb91b0ee289c8583dd59
https://cdn-avatars.huggingface.co/v1/production/uploads/640feb91b0ee289c8583dd59/CA4UZj2mEsJO2UR0NnN5H.jpeg
Hui Shen
user
Cloudriver
68c398f9fc1747b912403aae
Chengming Li
68c398f9fc1747b912403aaf
Lingpeng Kong
68c398f9fc1747b912403ab0
Ngai Wong
LongEmotion: Measuring Emotional Intelligence of Large Language Models
  in Long-Context Interaction
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/.
LongEmotion benchmark enhances large language models' emotional intelligence in long-context scenarios using Retrieval-Augmented Generation and Collaborative Emotional Modeling.
Large language models Emotional Intelligence long-context understanding LongEmotion Emotion Classification Emotion Detection Emotion QA Emotion Conversation Emotion Summary Emotion Expression Retrieval-Augmented Generation Collaborative Emotional Modeling GPT series