Understanding the capabilities, limitations, and societal impact of large language models

Very Large Language Models and How to Evaluate Them

Large Language Models: A New Moore's Law?

The Reformer - Pushing the limits of language modeling

Red-Teaming Large Language Models

The Curse of Multilinguality in Lexical Normalization

The paper investigates how many languages should be jointly trained in a single lexical normalization model. Using a fixed-capacity character-level model across twelve languages, it finds that accuracy peaks when a language is trained with only a few others—typically one to four—and then declines sharply as more languages are added, dropping about forty percent. A control experiment keeping total training data constant shows the decline is due to competition for model capacity rather than data scarcity, and no reliable typological rule predicts the optimal number of co‑training languages.

Introducing The World's Largest Open Multilingual Language Model: BLOOM