LongEmotion: Measuring Emotional Intelligence of Large Language Models in Long-Context Interaction
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866908756515749888 |
|---|---|
| author | Liu, Weichu Xiong, Jing Hu, Yuxuan Li, Zixuan Tan, Minghuan Mao, Ningning Shen, Hui Xu, Wendong Tao, Chaofan Yang, Min Li, Chengming Kong, Lingpeng Wong, Ngai |
| author_facet | Liu, Weichu Xiong, Jing Hu, Yuxuan Li, Zixuan Tan, Minghuan Mao, Ningning Shen, Hui Xu, Wendong Tao, Chaofan Yang, Min Li, Chengming Kong, Lingpeng Wong, Ngai |
| contents | Large language models (LLMs) have made significant progress in Emotional Intelligence (EI) and long-context modeling. However, existing benchmarks often overlook the fact that emotional information processing unfolds as a continuous long-context process. To address the absence of multidimensional EI evaluation in long-context inference and explore model performance under more challenging conditions, we present LongEmotion, a benchmark that encompasses a diverse suite of tasks targeting the assessment of models' capabilities in Emotion Recognition, Knowledge Application, and Empathetic Generation, with an average context length of 15,341 tokens. To enhance performance under realistic constraints, we introduce the Collaborative Emotional Modeling (CoEM) framework, which integrates Retrieval-Augmented Generation (RAG) and multi-agent collaboration to improve models' EI in long-context scenarios. We conduct a detailed analysis of various models in long-context settings, investigating how reasoning mode activation, RAG-based retrieval strategies, and context-length adaptability influence their EI performance. Our project page is: https://longemotion.github.io/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_07403 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LongEmotion: Measuring Emotional Intelligence of Large Language Models in Long-Context Interaction Liu, Weichu Xiong, Jing Hu, Yuxuan Li, Zixuan Tan, Minghuan Mao, Ningning Shen, Hui Xu, Wendong Tao, Chaofan Yang, Min Li, Chengming Kong, Lingpeng Wong, Ngai Computation and Language Large language models (LLMs) have made significant progress in Emotional Intelligence (EI) and long-context modeling. However, existing benchmarks often overlook the fact that emotional information processing unfolds as a continuous long-context process. To address the absence of multidimensional EI evaluation in long-context inference and explore model performance under more challenging conditions, we present LongEmotion, a benchmark that encompasses a diverse suite of tasks targeting the assessment of models' capabilities in Emotion Recognition, Knowledge Application, and Empathetic Generation, with an average context length of 15,341 tokens. To enhance performance under realistic constraints, we introduce the Collaborative Emotional Modeling (CoEM) framework, which integrates Retrieval-Augmented Generation (RAG) and multi-agent collaboration to improve models' EI in long-context scenarios. We conduct a detailed analysis of various models in long-context settings, investigating how reasoning mode activation, RAG-based retrieval strategies, and context-length adaptability influence their EI performance. Our project page is: https://longemotion.github.io/ |
| title | LongEmotion: Measuring Emotional Intelligence of Large Language Models in Long-Context Interaction |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2509.07403 |