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Martin Schrimpf

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Related publications (5)

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Driving and suppressing the human language network using large language models

Martin Schrimpf

Transformer models such as GPT generate human-like language and are predictive of human brain responses to language. Here, using functional-MRI-measured brain responses to 1,000 diverse sentences, we first show that a GPT-based encoding model can predict t ...

Nature Portfolio2024

Modeling Visual Impairments with Artificial Neural Networks: a Review

Martin Schrimpf

We present an approach to bridge the gap between the computational models of human vision and the clinical practice on visual impairments (VI). In a nutshell, we propose to connect advances in neuroscience and machine learning to study the impact of VI on ...

Ieee Computer Soc2023

Instruction-tuned LLMs with World Knowledge are More Aligned to the Human Brain

Antoine Bosselut, Syrielle Mathilde Marie Montariol, Martin Schrimpf, Badr Khaled Abdelrahman Alkhamissy

Instruction-tuning is a widely adopted finetuning method that enables large language models (LLMs) to generate output that more closely resembles human responses. However, no studies have shown that instruction-tuning actually teaches LLMs to process langu ...

2024

Let's move forward: Image-computable models and a common model evaluation scheme are prerequisites for a scientific understanding of human vision

Matthias Bethge, Martin Schrimpf

In the target article, Bowers et al. dispute deep artificial neural network (ANN) models as the currently leading models of human vision without producing alternatives. They eschew the use of public benchmarking platforms to compare vision models with the ...

Cambridge Univ Press2023

Fixing the problems of deep neural networks will require better training data and learning algorithms

Adrien Christophe Doerig, Matthias Bethge, Martin Schrimpf

Bowers et al. argue that deep neural networks (DNNs) are poor models of biological vision because they often learn to rival human accuracy by relying on strategies that differ markedly from those of humans. We show that this problem is worsening as DNNs ar ...

Cambridge Univ Press2023