There are few principles or guidelines to ensure evaluations of generative AI (GenAI) models and systems are effective. To help address this gap, we propose a set of general dimensions that capture critical choices involved in GenAI evaluation design. These dimensions include the evaluation setting, the task type, the input source, the interaction style, the duration, the metric type, and the scoring method. By situating GenAI evaluations within these dimensions, we aim to guide decision-making during GenAI evaluation design and provide a structure for comparing different evaluations. We illustrate the utility of the proposed set of general dimensions using two examples: a hypothetical evaluation of the fairness of a GenAI system and three real-world GenAI evaluations of biological threats.
@article{arxiv.2411.12709,
title = {Dimensions of Generative AI Evaluation Design},
author = {P. Alex Dow and Jennifer Wortman Vaughan and Solon Barocas and Chad Atalla and Alexandra Chouldechova and Hanna Wallach},
journal= {arXiv preprint arXiv:2411.12709},
year = {2024}
}
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NeurIPS 2024 Workshop on Evaluating Evaluations (EvalEval)