Can Large Language Models Help Multimodal Language Analysis? MMLA: A Comprehensive Benchmark

• URL: http://arxiv.org/abs/2504.16427v2
• Date: Thu, 24 Apr 2025 07:35:03 GMT
• Title: Can Large Language Models Help Multimodal Language Analysis? MMLA: A Comprehensive Benchmark
• Authors: Hanlei Zhang, Zhuohang Li, Yeshuang Zhu, Hua Xu, Peiwu Wang, Haige Zhu, Jie Zhou, Jinchao Zhang,
• Abstract summary: MMLA comprises over 61K multimodal utterances drawn from both staged and real-world scenarios.<n>We evaluate eight mainstream branches of LLMs and MLLMs using three methods: zero-shot inference, supervised fine-tuning, and instruction tuning.<n>Experiments reveal that even fine-tuned models achieve only about 60%70% accuracy, underscoring the limitations of current MLLMs in understanding complex human language.
• Score: 35.654523541347174
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal language analysis is a rapidly evolving field that leverages multiple modalities to enhance the understanding of high-level semantics underlying human conversational utterances. Despite its significance, little research has investigated the capability of multimodal large language models (MLLMs) to comprehend cognitive-level semantics. In this paper, we introduce MMLA, a comprehensive benchmark specifically designed to address this gap. MMLA comprises over 61K multimodal utterances drawn from both staged and real-world scenarios, covering six core dimensions of multimodal semantics: intent, emotion, dialogue act, sentiment, speaking style, and communication behavior. We evaluate eight mainstream branches of LLMs and MLLMs using three methods: zero-shot inference, supervised fine-tuning, and instruction tuning. Extensive experiments reveal that even fine-tuned models achieve only about 60%~70% accuracy, underscoring the limitations of current MLLMs in understanding complex human language. We believe that MMLA will serve as a solid foundation for exploring the potential of large language models in multimodal language analysis and provide valuable resources to advance this field. The datasets and code are open-sourced at https://github.com/thuiar/MMLA.

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