1 citations · 1 across the 3 of their papers we have counts for
3 papers
Ensembling Language Models with Sequential Monte Carlo
Robin Shing Moon Chan, Tianyu Liu, Samuel Kiegeland +5
Practitioners have access to an abundance of language models and prompting strategies for solving many language modeling tasks; yet prior work shows that modeling performance is hi…
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…
On Efficiently Representing Regular Languages as RNNs
Anej Svete, Robin Shing Moon Chan, Ryan Cotterell
Recent work by Hewitt et al. (2020) provides an interpretation of the empirical success of recurrent neural networks (RNNs) as language models (LMs). It shows that RNNs can efficie…