MMRC: A Large-Scale Benchmark for Understanding Multimodal Large Language Model in Real-World Conversation

• URL: http://arxiv.org/abs/2502.11903v2
• Date: Sat, 08 Mar 2025 12:36:34 GMT
• Title: MMRC: A Large-Scale Benchmark for Understanding Multimodal Large Language Model in Real-World Conversation
• Authors: Haochen Xue, Feilong Tang, Ming Hu, Yexin Liu, Qidong Huang, Yulong Li, Chengzhi Liu, Zhongxing Xu, Chong Zhang, Chun-Mei Feng, Yutong Xie, Imran Razzak, Zongyuan Ge, Jionglong Su, Junjun He, Yu Qiao,
• Abstract summary: This paper introduces MMRC, a benchmark for evaluating six core open-ended abilities of MLLMs.<n> Evaluations on 20 MLLMs in MMRC indicate an accuracy drop during open-ended interactions.<n>We propose a simple yet effective NOTE-TAKING strategy, which can record key information from the conversation and remind the model during its responses.
• Score: 52.35744453954844
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent multimodal large language models (MLLMs) have demonstrated significant potential in open-ended conversation, generating more accurate and personalized responses. However, their abilities to memorize, recall, and reason in sustained interactions within real-world scenarios remain underexplored. This paper introduces MMRC, a Multi-Modal Real-world Conversation benchmark for evaluating six core open-ended abilities of MLLMs: information extraction, multi-turn reasoning, information update, image management, memory recall, and answer refusal. With data collected from real-world scenarios, MMRC comprises 5,120 conversations and 28,720 corresponding manually labeled questions, posing a significant challenge to existing MLLMs. Evaluations on 20 MLLMs in MMRC indicate an accuracy drop during open-ended interactions. We identify four common failure patterns: long-term memory degradation, inadequacies in updating factual knowledge, accumulated assumption of error propagation, and reluctance to say no. To mitigate these issues, we propose a simple yet effective NOTE-TAKING strategy, which can record key information from the conversation and remind the model during its responses, enhancing conversational capabilities. Experiments across six MLLMs demonstrate significant performance improvements.

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