5 citations · 9 across the 20 of their papers we have counts for
5 papers · 1 filter
Disaggregation Reveals Hidden Training Dynamics: The Case of Agreement Attraction
James A. Michaelov, Catherine Arnett
Language models generally produce grammatical text, but they are more likely to make errors in certain contexts. Drawing on paradigms from psycholinguistics, we carry out a fine-gr…
Explaining and Mitigating Crosslingual Tokenizer Inequities
Catherine Arnett, Tyler A. Chang, Stella Biderman +1
The number of tokens it takes to encode parallel text in different languages is known to vary. These disparities are called token premiums. Having high token premiums leads to less…
Global PIQA: Evaluating Commonsense Reasoning Across 100+ Languages and Cultures
Tyler A. Chang, Catherine Arnett, Abdelrahman Sadallah +377
To date, there exist almost no culturally-specific evaluation benchmarks for large language models (LLMs) that cover a large number of languages and cultures. In this paper, we pre…
Evaluating Morphological Alignment of Tokenizers in 70 Languages
Catherine Arnett, Marisa Hudspeth, Brendan O'Connor
While tokenization is a key step in language modeling, with effects on model training and performance, it remains unclear how to effectively evaluate tokenizer quality. One propose…
On the Acquisition of Shared Grammatical Representations in Bilingual Language Models
Catherine Arnett, Tyler A. Chang, James A. Michaelov +1
Crosslingual transfer is crucial to contemporary language models' multilingual capabilities, but how it occurs is not well understood. We ask what happens to a monolingual language…