Linzen, Tal

Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference

McCoy, R. T., Pavlick, E., & Linzen, T. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 944

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Srivastava, A., Rastogi, A., Rao, A., Shoeb, A. A. M., Abid, A., Fisch, A., Brown, A. R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 569

Targeted Syntactic Evaluation of Language Models

Marvin, R., & Linzen, T. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Natural Language Processing TechniquesSyntax, Semantics, Linguistic Variation

Cited by 334

COGS: A Compositional Generalization Challenge Based on Semantic Interpretation

Kim, N., & Linzen, T. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Natural Language Processing TechniquesTopic Modeling

Cited by 153

How Can We Accelerate Progress Towards Human-like Linguistic Generalization?

Linzen, T. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 148

Do Language Models’ Words Refer?

Mandelkern, M., & Linzen, T. (2024) · Computational Linguistics

Syntax, Semantics, Linguistic VariationPhilosophy and Theoretical Science

Cited by 77

Findings of the BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora

Warstadt, A., Mueller, A., Choshen, L., Wilcox, E., Zhuang, C., Ciro, J., Mosquera, R., Paranjabe, B., Williams, A., Linzen, T., et al. (2023) · Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning

Topic ModelingNatural Language Processing Techniques

Cited by 74

How much do language models copy from their training data? evaluating linguistic novelty in text generation using RAVEN

McCoy, R. T., Smolensky, P., Linzen, T., Gao, J., & Celikyilmaz, A. (2023) · Transactions of the Association for Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 70

How much do language models copy from their training data? Evaluating linguistic novelty in text generation using RAVEN

McCoy, R. T., Smolensky, P., Linzen, T., Gao, J., & Celikyilmaz, A. (2021) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 70

Quantity doesn’t buy quality syntax with neural language models

van Schijndel, M., Mueller, A., & Linzen, T. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Natural Language Processing TechniquesTopic Modeling

Cited by 65

Evaluating Attribution in Dialogue Systems: The BEGIN Benchmark

Dziri, N., Rashkin, H., Linzen, T., & Reitter, D. (2021) · arXiv

Speech and dialogue systemsTopic Modeling

Cited by 47

Studying the Inductive Biases of RNNs with Synthetic Variations of Natural Languages

Ravfogel, S., Goldberg, Y., & Linzen, T. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Natural Language Processing TechniquesTopic Modeling

Cited by 39

The MultiBERTs: BERT Reproductions for Robustness Analysis

Sellam, T., Yadlowsky, S., Wei, J., Saphra, N., D'Amour, A., Linzen, T., Bastings, J., Turc, I., Eisenstein, J., Das, D., et al. (2021) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 37

Universal linguistic inductive biases via meta-learning

McCoy, R. T., Grant, E., Smolensky, P., Griffiths, T. L., & Linzen, T. (2020) · arXiv

Language Development and DisordersDomain Adaptation and Few-Shot Learning

Cited by 13

Bigger is not always better: The importance of human-scale language modeling for psycholinguistics

Wilcox, E. G., Hu, M. Y., Mueller, A., Warstadt, A., Choshen, L., Zhuang, C., Williams, A., Cotterell, R., & Linzen, T. (2025) · Journal of Memory and Language

Natural Language Processing TechniquesTopic Modeling

Cited by 9

Characterizing verbatim short-term memory in neural language models

Armeni, K., Honey, C., & Linzen, T. (2022) · Proceedings of the 26th Conference on Computational Natural Language Learning (CoNLL)

Topic ModelingNeurobiology of Language and Bilingualism

Cited by 4