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Found 7 papers, 3 papers with code
Investigating grammatical abstraction in language models using few-shot learning of novel noun gender
Language models were tasked with learning the gender of a novel noun embedding from a few examples in one grammatical agreement context and predicting agreement in another, unseen context.
Signatures of Bayesian inference emerge from energy efficient synapses
The resulting networks revealed a tradeoff between circuit performance and the energetic cost of synaptic reliability.
Social Value Orientation and Integral Emotions in Multi-Agent Systems
Human social behavior is influenced by individual differences in social preferences.
Beyond the limitations of any imaginable mechanism: large language models and psycholinguistics
Large language models are not detailed models of human linguistic processing.
Do LSTMs See Gender? Probing the Ability of LSTMs to Learn Abstract Syntactic Rules
However, we do not have a mechanistic understanding of how LSTMs perform such linguistic tasks.
Bayesian Modeling of Language-Evoked Event-Related Potentials
Bayesian hierarchical models are well-suited to analyzing the often noisy data from electroencephalography experiments in cognitive neuroscience: these models provide an intuitive framework to account for structures and correlations in the data, and they allow a straightforward handling of uncertainty.
Adaptive Estimators Show Information Compression in Deep Neural Networks
With two improved methods of estimation, firstly, we show that saturation of the activation function is not required for compression, and the amount of compression varies between different activation functions.
