2 citations · 5 across the 12 of their papers we have counts for
15 papers
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…
Priors in Time: Missing Inductive Biases for Language Model Interpretability
Ekdeep Singh Lubana, Can Rager, Sai Sumedh R. Hindupur +13
Recovering meaningful concepts from language model activations is a central aim of interpretability. While existing feature extraction methods aim to identify concepts that are ind…
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…
Model Connectomes: A Generational Approach to Data-Efficient Language Models
Klemen Kotar, Greta Tuckute
Biological neural networks are shaped both by evolution across generations and by individual learning within an organism's lifetime, whereas standard artificial neural networks und…
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…
How to optimize neuroscience data utilization and experiment design for advancing brain models of visual and linguistic cognition?
Greta Tuckute, Dawn Finzi, Eshed Margalit +8
In recent years, neuroscience has made significant progress in building large-scale artificial neural network (ANN) models of brain activity and behavior. However, there is no cons…
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…
JOSA: Joint surface-based registration and atlas construction of brain geometry and function
Jian Li, Greta Tuckute, Evelina Fedorenko +3
Surface-based cortical registration is an important topic in medical image analysis and facilitates many downstream applications. Current approaches for cortical registration are m…
Joint cortical registration of geometry and function using semi-supervised learning
Jian Li, Greta Tuckute, Evelina Fedorenko +3
Brain surface-based image registration, an important component of brain image analysis, establishes spatial correspondence between cortical surfaces. Existing iterative and learnin…