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cs.GT2024★ 2 cited
Pricing and Competition for Generative AI
Rafid Mahmood
Compared to classical machine learning (ML) models, generative models offer a new usage paradigm where (i) a single model can be used for many different tasks out-of-the-box; (ii)…
cs.HC2024
No Need to Sacrifice Data Quality for Quantity: Crowd-Informed Machine Annotation for Cost-Effective Understanding of Visual Data
Christopher Klugmann, Rafid Mahmood, Guruprasad Hegde +2
Labeling visual data is expensive and time-consuming. Crowdsourcing systems promise to enable highly parallelizable annotations through the participation of monetarily or otherwise…
cs.LG2024
AutoScale: Scale-Aware Data Mixing for Pre-Training LLMs
Feiyang Kang, Yifan Sun, Bingbing Wen +4
Domain reweighting is an emerging research area aimed at adjusting the relative weights of different data sources to improve the effectiveness and efficiency of LLM pre-training. W…