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Found 5 papers, 5 papers with code
Universal Neurons in GPT2 Language Models
In other words, are neural mechanisms universal across different models?
Training Dynamics of Contextual N-Grams in Language Models
We show that this neuron exists within a broader contextual n-gram circuit: we find late layer neurons which recognize and continue n-grams common in German text, but which only activate if the German neuron is active.
Language Models Represent Space and Time
The capabilities of large language models (LLMs) have sparked debate over whether such systems just learn an enormous collection of superficial statistics or a set of more coherent and grounded representations that reflect the real world.
Finding Neurons in a Haystack: Case Studies with Sparse Probing
Despite rapid adoption and deployment of large language models (LLMs), the internal computations of these models remain opaque and poorly understood.
Learning Sparse Nonlinear Dynamics via Mixed-Integer Optimization
Discovering governing equations of complex dynamical systems directly from data is a central problem in scientific machine learning.
