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This paper addresses foundational challenges in evaluating Generative Artificial Intelligence (GAI), focusing on the transition from expertise evaluation to intelligence evaluation. It critiques both quantitative and qualitative metrics for GAI, highlighting limitations in human-algorithm interaction environments. The study examines knowledge representation in neural network architectures and the processes of filtering versus tokenisation for image processing, emphasising inconsistencies and lack of standardisation in test design. Based on this finding the paper proposes a framework for future research to further explore the research questions.

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Can Image Generative Models Be Considered Experts?

  • 会议论文

  • 发表日期：

    2025年

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  • Dang Ba Hai Quang,
  • Ginel Dorleon,
  • Andrew Colarik

摘要

This paper addresses foundational challenges in evaluating Generative Artificial Intelligence (GAI), focusing on the transition from expertise evaluation to intelligence evaluation. It critiques both quantitative and qualitative metrics for GAI, highlighting limitations in human-algorithm interaction environments. The study examines knowledge representation in neural network architectures and the processes of filtering versus tokenisation for image processing, emphasising inconsistencies and lack of standardisation in test design. Based on this finding the paper proposes a framework for future research to further explore the research questions.