A Multi-To-One Interview Paradigm for Efficient MLLM Evaluation

• URL: http://arxiv.org/abs/2509.14886v1
• Date: Thu, 18 Sep 2025 12:07:40 GMT
• Title: A Multi-To-One Interview Paradigm for Efficient MLLM Evaluation
• Authors: Ye Shen, Junying Wang, Farong Wen, Yijin Guo, Qi Jia, Zicheng Zhang, Guangtao Zhai,
• Abstract summary: We propose a multi-to-one interview paradigm for efficient MLLM evaluation.<n>Our framework consists of (i) a two-stage interview strategy with pre-interview and formal interview phases, (ii) dynamic adjustment of weights to ensure fairness, and (iii) an adaptive mechanism for question difficulty-level chosen.
• Score: 63.76972456980632
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid progress of Multi-Modal Large Language Models (MLLMs) has spurred the creation of numerous benchmarks. However, conventional full-coverage Question-Answering evaluations suffer from high redundancy and low efficiency. Inspired by human interview processes, we propose a multi-to-one interview paradigm for efficient MLLM evaluation. Our framework consists of (i) a two-stage interview strategy with pre-interview and formal interview phases, (ii) dynamic adjustment of interviewer weights to ensure fairness, and (iii) an adaptive mechanism for question difficulty-level chosen. Experiments on different benchmarks show that the proposed paradigm achieves significantly higher correlation with full-coverage results than random sampling, with improvements of up to 17.6% in PLCC and 16.7% in SRCC, while reducing the number of required questions. These findings demonstrate that the proposed paradigm provides a reliable and efficient alternative for large-scale MLLM benchmarking.

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