ArtifactLinker frames SOTA discovery as missing-link prediction on an artifact graph of models and datasets, with a two-stage ranking-plus-verification pipeline and a new benchmark of 14k artifacts.
Navigating dataset documentations in ai: A large-scale analysis of dataset cards on hugging face.ArXiv, abs/2401.13822,
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
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.
Hyper-datafication in frontier AI increases resource consumption and redistributes environmental burdens, labor risks, and representational harms toward the Global South, data workers, and under-represented cultures, based on analysis of 550,000 Hugging Face datasets and Kenyan worker responses.
citing papers explorer
-
ArtifactLinker: Linking Scientific Artifacts for Automatic State-of-the-Art Discovery
ArtifactLinker frames SOTA discovery as missing-link prediction on an artifact graph of models and datasets, with a two-stage ranking-plus-verification pipeline and a new benchmark of 14k artifacts.
-
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.
-
How Hyper-Datafication Impacts the Sustainability Costs in Frontier AI
Hyper-datafication in frontier AI increases resource consumption and redistributes environmental burdens, labor risks, and representational harms toward the Global South, data workers, and under-represented cultures, based on analysis of 550,000 Hugging Face datasets and Kenyan worker responses.