3 citations · 8 across the 14 of their papers we have counts for
4 papers · 1 filter
Can Transformers Learn -gram Language Models?
Anej Svete, Nadav Borenstein, Mike Zhou +2
Much theoretical work has described the ability of transformers to represent formal languages. However, linking theoretical results to empirical performance is not straightforward…
Revealing Fine-Grained Values and Opinions in Large Language Models
Dustin Wright, Arnav Arora, Nadav Borenstein +3
Uncovering latent values and opinions embedded in large language models (LLMs) can help identify biases and mitigate potential harm. Recently, this has been approached by prompting…
What Languages are Easy to Language-Model? A Perspective from Learning Probabilistic Regular Languages
Nadav Borenstein, Anej Svete, Robin Chan +5
What can large language models learn? By definition, language models (LM) are distributions over strings. Therefore, an intuitive way of addressing the above question is to formali…
Investigating Human Values in Online Communities
Nadav Borenstein, Arnav Arora, Lucie-Aimée Kaffee +1
Studying human values is instrumental for cross-cultural research, enabling a better understanding of preferences and behaviour of society at large and communities therein. To stud…