1 citations · 1 across the 5 of their papers we have counts for
5 papers
Beyond Decodability: Reconstructing Language Model Representations with an Encoding Probe
Gaofei Shen, Martijn Bentum, Tomas O. Lentz +2
Probing is widely used to study which features can be decoded from language model representations. However, the common decoding probe approach has two limitations that we aim to so…
Tracking the emergence of linguistic structure in self-supervised models learning from speech
Marianne de Heer Kloots, Martijn Bentum, Hosein Mohebbi +3
Self-supervised speech models learn effective representations of spoken language, which have been shown to reflect various aspects of linguistic structure. But when does such struc…
What do self-supervised speech models know about Dutch? Analyzing advantages of language-specific pre-training
Marianne de Heer Kloots, Hosein Mohebbi, Charlotte Pouw +3
How language-specific are speech representations learned by self-supervised models? Existing work has shown that a range of linguistic features can be successfully decoded from end…
On the reliability of feature attribution methods for speech classification
Gaofei Shen, Hosein Mohebbi, Arianna Bisazza +2
As the capabilities of large-scale pre-trained models evolve, understanding the determinants of their outputs becomes more important. Feature attribution aims to reveal which parts…
Encoding of lexical tone in self-supervised models of spoken language
Gaofei Shen, Michaela Watkins, Afra Alishahi +2
Interpretability research has shown that self-supervised Spoken Language Models (SLMs) encode a wide variety of features in human speech from the acoustic, phonetic, phonological,…