Title: A Multi-To-One Interview Paradigm for Efficient MLLM Evaluation Show Chinese title

Title: 一种多对一访谈范式用于高效MLLM评估

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. Show Chinese abstract

Abstract: 多模态大语言模型（MLLMs）的快速发展推动了众多基准测试的创建。 然而，传统的全覆盖问答评估存在高冗余和低效率的问题。 受人类面试过程的启发，我们提出了一种多对一的面试范式，用于高效的MLLM评估。 我们的框架包括（i）包含预面试和正式面试阶段的两阶段面试策略， （ii）动态调整面试官权重以确保公平性，以及（iii）选择问题难度级别的自适应机制。 在不同基准上的实验表明，所提出的范式与全覆盖结果相比显著提高了相关性，PLCC提升了高达17.6%，SRCC提升了16.7%，同时减少了所需问题的数量。 这些发现表明，所提出的范式为大规模MLLM基准测试提供了一个可靠且高效的替代方案。