10 papers
CommonLID: Re-evaluating State-of-the-Art Language Identification Performance on Web Data
Pedro Ortiz Suarez, Laurie Burchell, Catherine Arnett +94
Language identification (LID) is a fundamental step in curating multilingual corpora. However, LID models still perform poorly for many languages, especially on the noisy and heter…
Position: Don't Just "Fix it in Post": A Science of AI Must Study Training Dynamics
Stella Biderman, Mohammad Aflah Khan, Niloofar Mireshghallah +3
What would it mean to have a scientific understanding of AI? Models are not static objects: they are snapshots of time-evolving processes shaped by data, objectives, architectures,…
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…
Goldfish: Monolingual Language Models for 350 Languages
Tyler A. Chang, Catherine Arnett, Zhuowen Tu +1
For many low-resource languages, the only available language models are large multilingual models trained on many languages simultaneously. Despite state-of-the-art performance on…
How Open Must Language Models be to Enable Reliable Scientific Inference?
James A. Michaelov, Catherine Arnett, Tyler A. Chang +7
How does the extent to which a model is open or closed impact the scientific inferences that can be drawn from research that involves it? In this paper, we analyze how restrictions…
Weight Tying Biases Token Embeddings Towards the Output Space
Antonio Lopardo, Avyukth Harish, Catherine Arnett +1
Weight tying, i.e. sharing parameters between input and output embedding matrices, is common practice in language model design, yet its impact on the learned embedding space remain…