Reichart, Roi

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

The Hitchhiker’s Guide to Testing Statistical Significance in Natural Language Processing

Dror, R., Baumer, G., Shlomov, S., & Reichart, R. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Natural Language Processing TechniquesTopic Modeling

Cited by 384

Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond

Feder, A., Keith, K. A., Manzoor, E., Pryzant, R., Sridhar, D., Wood-Doughty, Z., Eisenstein, J., Grimmer, J., Reichart, R., Roberts, M. E., et al. (2022) · Transactions of the Association for Computational Linguistics

Advanced Causal Inference TechniquesComputational and Text Analysis Methods

Cited by 220

Neural Structural Correspondence Learning for Domain Adaptation

Ziser, Y., & Reichart, R. (2017) · Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)

Domain Adaptation and Few-Shot LearningSentiment Analysis and Opinion Mining

Cited by 122

Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?

Gekhman, Z., Yona, G., Aharoni, R., Eyal, M., Feder, A., Reichart, R., & Herzig, J. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 86

Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?

Gekhman, Z., Yona, G., Aharoni, R., Eyal, M., Feder, A., Reichart, R., & Herzig, J. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 86

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

Replicability Analysis for Natural Language Processing: Testing Significance with Multiple Datasets

Dror, R., Baumer, G., Bogomolov, M., & Reichart, R. (2017) · Transactions of the Association for Computational Linguistics

Natural Language Processing TechniquesTopic Modeling

Cited by 63

PADA: Example-based Prompt Learning for on-the-fly Adaptation to Unseen Domains

Ben-David, E., Oved, N., & Reichart, R. (2022) · Transactions of the Association for Computational Linguistics

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 54

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

Your Prompt is My Command: On Assessing the Human-Centred Generality of Multimodal Models

Schellaert, W., Martínez-Plumed, F., Vold, K., Burden, J., A. M. Casares, P., Sheng Loe, B., Reichart, R., Ó hÉigeartaigh, S., Korhonen, A., & Hernández-Orallo, J. (2023) · Journal of Artificial Intelligence Research

Multimodal Machine Learning ApplicationsArtificial Intelligence Applications

Cited by 19

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

On the Robustness of Dialogue History Representation in Conversational Question Answering: A Comprehensive Study and a New Prompt-based Method

Gekhman, Z., Oved, N., Keller, O., Szpektor, I., & Reichart, R. (2023) · Transactions of the Association for Computational Linguistics

Topic ModelingSpeech and dialogue systems

Cited by 9