4 citations · 4 across the 2 of their papers we have counts for
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cs.AI2026
AI Exposure Scores: what they measure, what they miss, and what comes next
Campbell Lund, Thomas Euyang, Zanele Munyikwa +1
A set of exposure scores calculated in 2023 has become a central empirical input to the future of work debate. Produced by Eloundou et al. (2023) and referred to here as the GPTs a…
cs.AI2024★ 4 cited
Bridging the Data Provenance Gap Across Text, Speech and Video
Shayne Longpre, Nikhil Singh, Manuel Cherep +40
Progress in AI is driven largely by the scale and quality of training data. Despite this, there is a deficit of empirical analysis examining the attributes of well-established data…