Human-Centric Evaluation for Foundation Models
This addresses the need for better subjective evaluation methods in AI to improve LLM development for research and practical scenarios, though it is incremental in enhancing existing methodologies.
The paper tackles the problem that current evaluations of foundation models rely too heavily on objective metrics, failing to capture authentic human experiences, by proposing a Human-Centric subjective Evaluation (HCE) framework and conducting over 540 participant-driven evaluations, revealing Grok 3's superior performance followed by Deepseek R1 and Gemini 2.5, with OpenAI o3 mini lagging behind.
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.