AdaQE-CG uses context-aware adaptive query expansion and inter-card knowledge transfer from a MetaGAI Pool to generate higher-quality model and data cards than prior methods, validated on the new expert-annotated MetaGAI-Bench.
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Collaborative ML reproducibility requires socio-technical interactional support beyond artifacts, demonstrated via a clinical deployment and addressed by a proposed two-layer system with an AI semantic interface.
citing papers explorer
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AdaQE-CG: Adaptive Query Expansion for Web-Scale Generative AI Model and Data Card Generation
AdaQE-CG uses context-aware adaptive query expansion and inter-card knowledge transfer from a MetaGAI Pool to generate higher-quality model and data cards than prior methods, validated on the new expert-annotated MetaGAI-Bench.
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Reproducibility Beyond Artifacts: Interactional Support for Collaborative Machine Learning
Collaborative ML reproducibility requires socio-technical interactional support beyond artifacts, demonstrated via a clinical deployment and addressed by a proposed two-layer system with an AI semantic interface.