QQJ: Quantifying Qualitative Judgment for Scalable and Human-Aligned Evaluation of Generative AI
The paper introduces Quantifying Qualitative Judgment (QQJ), a new framework for evaluating generative AI. QQJ aims to bridge the gap between human judgment and automated assessment by using expert-designed rubrics. The framework demonstrates improved alignment with human evaluations and stability across diverse generative tasks.
- ▪QQJ addresses limitations in existing evaluation methodologies for generative AI, particularly in creative and human-facing tasks.
- ▪The framework separates the definition of quality from its execution, allowing for scalable and interpretable evaluations.
- ▪Extensive experiments show that QQJ aligns better with human judgment compared to traditional metrics and LLM-based evaluators.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Artificial Intelligence arXiv:2605.17382 (cs) [Submitted on 17 May 2026] Title:QQJ: Quantifying Qualitative Judgment for Scalable and Human-Aligned Evaluation of Generative AI Authors:Marjan Veysi, Pirooz Shamsinejadbabaki, Mohammad Zare, Mohammad Sabouri View a PDF of the paper titled QQJ: Quantifying Qualitative Judgment for Scalable and Human-Aligned Evaluation of Generative AI, by Marjan Veysi and 3 other authors View PDF Abstract:The rapid progress of generative artificial intelligence has exposed fundamental limitations in existing evaluation methodologies, particularly for open-ended, creative, and human-facing tasks.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
Discussion
0 commentsMore from arXiv cs.AI
