← 返回论文检索
ACL 2025aclfindings

SPHERE: An Evaluation Card for Human-AI Systems

Dora Zhao, Qianou Ma, Xinran Zhao, Chenglei Si, Chenyang Yang, Ryan Louie, Ehud Reiter, Diyi Yang, Tongshuang Wu

Stanford University · CMU, Carnegie Mellon University · Carnegie Mellon University · University of Aberdeen · School of Computer Science, Carnegie Mellon University

PDF 由论文原始站点提供,PaperCompass 不保存论文文件。DOI 10.18653/v1/2025.findings-acl.70 ↗

摘要

In the era of Large Language Models (LLMs), establishing effective evaluation methods and standards for diverse human-AI interaction systems is increasingly challenging. To encourage more transparent documentation and facilitate discussion on human-AI system evaluation design options, we present an evaluation card SPHERE, which encompasses five key dimensions: 1) What is being evaluated?; 2) How is the evaluation conducted?; 3) Who is participating in the evaluation?; 4) When is evaluation conducted?; 5) How is evaluation validated? We conduct a review of 39 human-AI systems using SPHERE, outlining current evaluation practices and areas for improvement. We provide three recommendations for improving the validity and rigor of evaluation practices.