Goldstein, Ariel

Shared computational principles for language processing in humans and deep language models

Goldstein, A., Zada, Z., Buchnik, E., Schain, M., Price, A., Aubrey, B., Nastase, S. A., Feder, A., Emanuel, D., Cohen, A., et al. (2022) · Nature Neuroscience

Neurobiology of Language and BilingualismReading and Literacy Development

Cited by 447

Direct Fit to Nature: An Evolutionary Perspective on Biological and Artificial Neural Networks

Hasson, U., Nastase, S. A., & Goldstein, A. (2020) · Neuron

Neural Networks and ApplicationsEmbodied and Extended Cognition

Cited by 387

Alignment of brain embeddings and artificial contextual embeddings in natural language points to common geometric patterns

Goldstein, A., Grinstein-Dabush, A., Schain, M., Wang, H., Hong, Z., Aubrey, B., Nastase, S. A., Zada, Z., Ham, E., Feder, A., et al. (2024) · Nature Communications

Neurobiology of Language and BilingualismTopic Modeling

Cited by 64

Systematic Biases in LLM Simulations of Debates

Taubenfeld, A., Dover, Y., Reichart, R., & Goldstein, A. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing

Ethics and Social Impacts of AIOpinion Dynamics and Social Influence

Cited by 47

Incremental accumulation of linguistic context in artificial and biological neural networks

Tikochinski, R., Goldstein, A., Meiri, Y., Hasson, U., & Reichart, R. (2025) · Nature Communications

Neurobiology of Language and BilingualismNeural dynamics and brain function

Cited by 24

Perspective changes in human listeners are aligned with the contextual transformation of the word embedding space

Tikochinski, R., Goldstein, A., Yeshurun, Y., Hasson, U., & Reichart, R. (2023) · Cerebral Cortex

Neurobiology of Language and BilingualismTopic Modeling

Cited by 14

Confidence Improves Self-Consistency in LLMs

Taubenfeld, A., Sheffer, T., Ofek, E., Feder, A., Goldstein, A., Gekhman, Z., & Yona, G. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 13

Scale matters: Large language models with billions (rather than millions) of parameters better match neural representations of natural language

Hong, Z., Wang, H., Zada, Z., Gazula, H., Turner, D., Aubrey, B., Niekerken, L., Doyle, W., Devore, S., Dugan, P., et al. (2024) · eLife

Neurobiology of Language and BilingualismTopic Modeling

Cited by 1