AI-Assisted Systematization for Evaluating GenAI Systems

Problem
This preprint addresses the gap in the evaluation of generative AI (GenAI) systems, particularly the ambiguity surrounding broad concepts such as “reasoning,” “fairness,” and “creativity.” The authors highlight that the lack of a structured approach to systematization leads to difficulties in defining measurable evaluation criteria and interpreting results. This work aims to provide a systematic framework for translating these broad concepts into explicit, measurable terms, thereby facilitating more effective evaluations of GenAI systems.

Method
The authors propose a structured representation called a “concept spec,” which serves as a formalized account of a systematized concept. To assist in the creation of these concept specs, they develop two AI-assisted systematizers: a direct zero-shot approach and a multi-agent approach. The zero-shot method leverages existing AI capabilities to generate concept specs without prior examples, while the multi-agent approach simulates a collaborative manual systematization process, drawing on established methodologies from the literature. The validation of the generated concept specs is conducted using a validation worksheet that assesses content validity and information recoverability, ensuring that the outputs are both relevant and useful for evaluation purposes.

Results
The authors apply their AI-assisted systematizers to two specific concepts: hate-based rhetoric and digital empathy. They report that the generated concept specs demonstrate high content validity, with qualitative assessments indicating that the specs accurately capture the essential attributes of the concepts. Additionally, the information recoverability of the specs is evaluated, showing that users can effectively retrieve relevant information from the generated outputs. While specific quantitative metrics are not disclosed, the qualitative results suggest a significant improvement in the clarity and usability of evaluation criteria compared to traditional methods.

Limitations
The authors acknowledge that their approach may still be limited by the inherent biases present in the AI models used for systematization, which could affect the neutrality of the generated concept specs. Furthermore, the reliance on AI assistance may not fully replicate the nuanced understanding that human evaluators bring to complex concepts. The study is also constrained by its focus on only two concepts, which may not generalize across the broader landscape of GenAI evaluations. Additionally, the validation process is qualitative, which may not capture all dimensions of validity.

Why it matters
This work has significant implications for the field of AI evaluation, as it provides a structured methodology for systematizing complex concepts that are critical for assessing GenAI systems. By facilitating clearer definitions and measurable criteria, the proposed framework can enhance the rigor and reproducibility of evaluations in AI research. This could lead to more reliable assessments of GenAI capabilities, ultimately informing the development of more ethical and effective AI systems. The approach also opens avenues for future research into automated evaluation frameworks and the integration of AI in the evaluation process.

Authors: Dhruv Agarwal, Emily Sheng, Chad Atalla, Jean Garcia-Gathright, Hussein Mozannar, Hannah Washington, Alexandra Chouldechova, Solon Baracas et al.
Source: arXiv:2605.26001
URL: https://arxiv.org/abs/2605.26001v1