2 citations · 5 across the 12 of their papers we have counts for
10 papers · 1 filter
Interpreting Brain Responses to Language with Sparse Features from Language Models
Michael A. Lepori, Kendrick Kay, Greta Tuckute
A central goal of cognitive neuroscience is to characterize the features that are represented by human language cortex. Artificial language models (LMs) have emerged as a powerful…
Different types of syntactic agreement recruit the same units within large language models
Daria Kryvosheieva, Andrea de Varda, Evelina Fedorenko +1
Large language models (LLMs) can reliably distinguish grammatical from ungrammatical sentences, but how grammatical knowledge is represented within the models remains an open quest…
Topoformer: brain-like topographic organization in Transformer language models through spatial querying and reweighting
Taha Binhuraib, Greta Tuckute, Nicholas Blauch
Spatial functional organization is a hallmark of biological brains: neurons are arranged topographically according to their response properties, at multiple scales. In contrast, re…
Modeling the language cortex with form-independent and enriched representations of sentence meaning reveals remarkable semantic abstractness
Shreya Saha, Shurui Li, Greta Tuckute +5
The human language system represents both linguistic forms and meanings, but the abstractness of the meaning representations remains debated. Here, we searched for abstract represe…
Representing Speech Through Autoregressive Prediction of Cochlear Tokens
Greta Tuckute, Klemen Kotar, Evelina Fedorenko +1
We introduce AuriStream, a biologically inspired model for encoding speech via a two-stage framework inspired by the human auditory processing hierarchy. The first stage transforms…
Language models align with brain regions that represent concepts across modalities
Maria Ryskina, Greta Tuckute, Alexander Fung +2
Cognitive science and neuroscience have long faced the challenge of disentangling representations of language from representations of conceptual meaning. As the same problem arises…
From Language to Cognition: How LLMs Outgrow the Human Language Network
Badr AlKhamissi, Greta Tuckute, Yingtian Tang +3
Large language models (LLMs) exhibit remarkable similarity to neural activity in the human language network. However, the key properties of language shaping brain-like representati…
The LLM Language Network: A Neuroscientific Approach for Identifying Causally Task-Relevant Units
Badr AlKhamissi, Greta Tuckute, Antoine Bosselut +1
Large language models (LLMs) exhibit remarkable capabilities on not just language tasks, but also various tasks that are not linguistic in nature, such as logical reasoning and soc…
Brain-Like Language Processing via a Shallow Untrained Multihead Attention Network
Badr AlKhamissi, Greta Tuckute, Antoine Bosselut +1
Large Language Models (LLMs) have been shown to be effective models of the human language system, with some models predicting most explainable variance of brain activity in current…
WhisBERT: Multimodal Text-Audio Language Modeling on 100M Words
Lukas Wolf, Greta Tuckute, Klemen Kotar +4
Training on multiple modalities of input can augment the capabilities of a language model. Here, we ask whether such a training regime can improve the quality and efficiency of the…