Why do small language models underperform? Studying Language Model Saturation via the Softmax Bottleneck

arXiv:2404.07647v1 Announce Type: new
Abstract: Recent advances in language modeling consist in pretraining highly parameterized neural networks on extremely large web-mined text corpora. Training and inference with such models can be costly in practice, which incentivizes the use of smaller counterparts. However, it has been observed that smaller models can suffer from saturation, characterized as a drop in performance at some advanced point in training followed by a plateau. In this paper, we find that such saturation can be explained …

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