Despite major advances in multilingual modeling, large quality disparities persist across languages. Besides the obvious impact of uneven training resources, typological properties have also been prop...

How do you build Large Language Models? How do humans experience Natural Language Processing (NLP) applications in their daily lives? And how can we...

We introduce TurBLiMP, the first Turkish benchmark of linguistic minimal pairs, designed to evaluate the linguistic abilities of monolingual and multilingual language models (LMs). Covering 16 linguis...

Seminal work by Huebner et al. (2021) showed that language models (LMs) trained on English Child-Directed Language (CDL) can reach similar syntactic abilities as LMs trained on much larger amounts of ...

A former student of mine has penned an attack on universities, derived from their own disappointing experience studying Politics and International Relations at the place where I ply my trade. In short...

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Extremely low-resource (XLR) languages lack substantial corpora for training NLP models, motivating the use of all available resources such as dictionaries and grammar books. Machine Translation from ...

It’s certainly an exciting time to study communication in whales and dolphins.

Abstract. This paper introduces Filtered Corpus Training, a method that trains language models (LMs) on corpora with certain linguistic constructions filtered out from the training data, and uses it t...