AI RESEARCH
EmoBench-M: Benchmarking Emotional Intelligence for Multimodal Large Language Models
arXiv CS.AI
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ArXi:2502.04424v4 Announce Type: replace-cross With the integration of multimodal large language models (MLLMs) into robotic systems and AI applications, embedding emotional intelligence (EI) capabilities is essential for enabling these models to perceive, interpret, and respond to human emotions effectively in real-world scenarios. Existing static, text-based, or text-image benchmarks overlook the multimodal complexities of real interactions and fail to capture the dynamic, context-dependent nature of emotional expressions, rendering them inadequate for evaluating MLLMs' EI capabilities.