Developing and Maintaining an Open-Source Repository of AI Evaluations: Challenges and Insights
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866913933970898944 |
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| author | Abbas, Alexandra Waggoner, Celia Olive, Justin |
| author_facet | Abbas, Alexandra Waggoner, Celia Olive, Justin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_06893 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Developing and Maintaining an Open-Source Repository of AI Evaluations: Challenges and Insights Abbas, Alexandra Waggoner, Celia Olive, Justin Computation and Language Artificial Intelligence 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. |
| title | Developing and Maintaining an Open-Source Repository of AI Evaluations: Challenges and Insights |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2507.06893 |