EIBench: Assessing the Emotion Interpretation ability of Vision Large Language Models

Yuxiang Lin, Jue Wang, Haomin Liang, Zebang Cheng, Jun-Yan He, Zhi-Qi Cheng, Xiaojiang Peng, Xian-Sheng Hua

Keywords: Emotion Interpretation, Large Language Model

Abstract: Affect computing is crucial in fields such as human-computer interaction, healthcare, and market research, yet emotion's ambiguity and subjectivity challenge current recognition techniques. We propose Emotion Interpretation (EI), a task that interprets the reasons behind emotions, and create the Emotion Interpretation Benchmark (EIBench) using a VLLM-assisted dataset construction method, Coarse-to-Fine Self-Ask (CFSA), with carefully human in-the-loop annotation. EIBench includes 1,615 basic and 50 multi-faceted complex emotion interpretation samples. Experiments show limited proficiency of existing models in EI, with the best achieving 62.41% accuracy in the zero-shot setting and some performing lower than the text-only LLaMA-3 model (6.26%) in the caption-provided setting. Different personas assigned also differ the benchmark results. Overcoming the challenges posed by EI can result in more empathetic AI systems, thereby enhancing human-computer interaction and emotion-sensitive applications.

Primary Area: datasets and benchmarks

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Submission Number: 6604