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cs.CL2023
Inverse Scaling: When Bigger Isn't Better
Ian R. McKenzie, Alexander Lyzhov, Michael Pieler +24
Work on scaling laws has found that large language models (LMs) show predictable improvements to overall loss with increased scale (model size, training data, and compute). Here, w…
cs.CL2019
Sampling Bias in Deep Active Classification: An Empirical Study
Ameya Prabhu, Charles Dognin, Maneesh Singh
The exploding cost and time needed for data labeling and model training are bottlenecks for training DNN models on large datasets. Identifying smaller representative data samples w…