2025/07/09 by Abbas, Alexandra, Celia Waggoner, Waggoner, Celia +2 · 1 citation
Computer Science · Decision Sciences · Medicine · #Artificial Intelligence (cs.AI) #Artificial Intelligence in Healthcare and Education #Computation and Language (cs.CL) #FOS: Computer and information sciences #Scientific Computing and Data Management #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2507.06893
openalex publication_date 2025/07/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
AI evaluations have become critical tools for assessing large language model capabilities and safety. This paper presents practical insights from eight months of maintaining inspect_evals, an open-source repository of 70+ community-contributed AI evaluations. We identify key challenges in implementing and maintaining AI evaluations and develop solutions including: (1) a structured cohort management framework for scaling community contributions, (2) statistical methodologies for optimal resampling and cross-model comparison with uncertainty quantification, and (3) systematic quality control processes for reproducibility. Our analysis reveals that AI evaluation requires specialized infrastructure, statistical rigor, and community coordination beyond traditional software development practices.