Junying Wang

Yijin Guo, Kaiyuan Ji, Xiaorong Zhu et al.

Currently, nearly all evaluations of foundation models focus on objective metrics, emphasizing quiz performance to define model capabilities. While this model-centric approach enables rapid performance assessment, it fails to reflect authentic human experiences. To address this gap, we propose a Human-Centric subjective Evaluation (HCE) framework, focusing on three core dimensions: problem-solving ability, information quality, and interaction experience. Through experiments involving Deepseek R1, OpenAI o3 mini, Grok 3, and Gemini 2.5, we conduct over 540 participant-driven evaluations, where humans and models collaborate on open-ended research tasks, yielding a comprehensive subjective dataset. This dataset captures diverse user feedback across multiple disciplines, revealing distinct model strengths and adaptability. Our findings highlight Grok 3's superior performance, followed by Deepseek R1 and Gemini 2.5, with OpenAI o3 mini lagging behind. By offering a novel framework and a rich dataset, this study not only enhances subjective evaluation methodologies but also lays the foundation for standardized, automated assessments, advancing LLM development for research and practical scenarios. Our dataset link is https://github.com/yijinguo/Human-Centric-Evaluation.

Ye Shen, Junying Wang, Farong Wen et al.

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.