133 citations · 307 across the 28 of their papers we have counts for
29 papers
Modeling semantic association in self-paced reading with language model embeddings
Sara Møller Østergaard, Kenneth Enevoldsen, Afra Alishahi +1
Semantic association between a word and its context has been identified as an important component of reading comprehension, even when word predictability is accounted for. Recent r…
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…
In-Context Learning in Speech Language Models: Analyzing the Role of Acoustic Features, Linguistic Structure, and Induction Heads
Charlotte Pouw, Hosein Mohebbi, Afra Alishahi +1
In-Context Learning (ICL) has been extensively studied in text-only Language Models, but remains largely unexplored in the speech domain. Here, we investigate how linguistic and ac…
Gender Disambiguation in Machine Translation: Diagnostic Evaluation in Decoder-Only Architectures
Chiara Manna, Hosein Mohebbi, Afra Alishahi +2
While Large Language Models achieve state-of-the-art results across a wide range of NLP tasks, they remain prone to systematic biases. Among these, gender bias is particularly sali…
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…
Are We Paying Attention to Her? Investigating Gender Disambiguation and Attention in Machine Translation
Chiara Manna, Afra Alishahi, Frédéric Blain +1
While gender bias in modern Neural Machine Translation (NMT) systems has received much attention, traditional evaluation metrics do not to fully capture the extent to which these s…