BLiMP: the benchmark of linguistic minimal pairs for English

Attention Is All You Need

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Long Short-Term Memory

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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

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ASPECTS OF THE THEORY OF SYNTAX

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Deep Contextualized Word Representations

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Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

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Lectures on Government and Binding

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Recurrent neural network based language model

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GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

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Universal Language Model Fine-tuning for Text Classification

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Transformer-XL: Attentive language models beyond a fixed-length context

Dai, Z., Yang, Z., Yang, Y., Carbonell, J., Le, Q., & Salakhutdinov, R. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1285 · Cited by 3,169 · see citing works

An empirical study of smoothing techniques for language modeling

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KenLM: Faster and Smaller Language Model Queries

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Neural Network Acceptability Judgments

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Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies

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Core Syntax

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Core Syntax: A Minimalist Approach

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Broken agreement

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Colorless Green Recurrent Networks Dream Hierarchically

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The Empirical Base of Linguistics: Grammaticality Judgments and Linguistic Methodology

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Logic in Grammar

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Scalable Modified Kneser-Ney Language Model Estimation

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Targeted Syntactic Evaluation of Language Models

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Verbal argument structure: Events and participants

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What you can cram into a single vector: Probing sentence embeddings for linguistic propertiesPreprint

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Does String-Based Neural MT Learn Source Syntax?

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Grammaticality, Acceptability, and Probability: A Probabilistic View of Linguistic Knowledge

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Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction TasksPreprint

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Definiteness and the English Existential

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Uncovering the Richness of the Stimulus: Structure Dependence and Indirect Statistical Evidence

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What do RNN Language Models Learn about Filler–Gap Dependencies?

Wilcox, E., Lévy, R., Morita, T., & Futrell, R. (2018) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/w18-5423 · Cited by 159 · see citing works

At Least et al.: The Semantics of Scalar Modifiers

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An Introduction to Syntactic Analysis and Theory

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Investigating variation in island effects

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Investigating BERT’s Knowledge of Language: Five Analysis Methods with NPIs

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Predicting Grammaticality on an Ordinal Scale

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jiant: A Software Toolkit for Research on General-Purpose Text Understanding Models

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Do Language Models Understand Anything? On the Ability of LSTMs to Understand Negative Polarity Items

Jumelet, J., & Hupkes, D. (2018) · doi:10.18653/v1/w18-5424 · Cited by 63 · see citing works

RNN Simulations of Grammaticality Judgments on Long-distance Dependencies

Chowdhury, S. A., & Zamparelli, R. (2018) · Iris (University of Trento) · Cited by 58 · see citing works

Structural Supervision Improves Learning of Non-Local Grammatical Dependencies

Wilcox, E., Qian, P., Futrell, R., Ballesteros, M., & Lévy, R. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1334 · Cited by 57 · see citing works

RNNs as psycholinguistic subjects: Syntactic state and grammatical dependencyPreprint

Futrell, R., Wilcox, E., Morita, T., & Lévy, R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.01329 · Cited by 50 · see citing works

Verb Argument Structure Alternations in Word and Sentence EmbeddingsPreprint

Kann, K., Warstadt, A., Williams, A., & Bowman, S. R. (2019) · arXiv (Cornell University) · doi:10.7275/q5js-4y86 · Cited by 34 · see citing works

Linguistic Analysis of Pretrained Sentence Encoders with Acceptability JudgmentsPreprint

Warstadt, A., & Bowman, S. R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.03438 · Cited by 31 · see citing works

Grammatical Analysis of Pretrained Sentence Encoders with Acceptability Judgments.Preprint

Warstadt, A., & Bowman, S. R. (2019) · arXiv (Cornell University) · Cited by 19 · see citing works

What Don't RNN Language Models Learn About Filler-Gap Dependencies?

Chaves, R. P. (2020) · ScholarWorks@UMassAmherst (University of Massachusetts Amherst) · doi:10.7275/f7yj-1n62 · Cited by 18 · see citing works

Assessing the ability of Transformer-based Neural Models to represent structurally unbounded dependencies

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An LSTM Adaptation Study of (Un)grammaticality

Chowdhury, S. A., & Zamparelli, R. (2019) · doi:10.18653/v1/w19-4821 · Cited by 7 · see citing works

Representation of Constituents in Neural Language Models: Coordination Phrase as a Case StudyPreprint

An, A., Qian, P., Wilcox, E., & Lévy, R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.04625 · Cited by 3 · see citing works

Dissociating language and thought in large language models

Mahowald, K., Ivanova, A. A., Blank, I. A., Kanwisher, N., Tenenbaum, J. B., & Fedorenko, E. (2024) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2024.01.011 · Cited by 345 · see citing works

Evaluating Models’ Local Decision Boundaries via Contrast Sets

Gardner, M., Artzi, Y., Basmov, V., Berant, J., Bogin, B., Chen, S., Dasigi, P., Dua, D., Elazar, Y., Gottumukkala, A., et al. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.117 · Cited by 265 · see citing works

Dynabench: Rethinking Benchmarking in NLP

Kiela, D., Bartolo, M., Nie, Y., Kaushik, D., Geiger, A., Wu, Z., Vidgen, B., Prasad, G., Singh, A., Ringshia, P., et al. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.324 · Cited by 254 · see citing works

Foundation metrics for evaluating effectiveness of healthcare conversations powered by generative AI

Abbasian, M., Khatibi, E., Azimi, I., Oniani, D., Shakeri Hossein Abad, Z., Thieme, A., Sriram, R., Yang, Z., Wang, Y., Lin, B., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01074-z · Cited by 213 · see citing works

Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Little

Sinha, K., Jia, R., Hupkes, D., Pineau, J., Williams, A., & Kiela, D. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.230 · Cited by 204 · see citing works

MetaICL: Learning to Learn In Context

Min, S., Lewis, M., Zettlemoyer, L., & Hajishirzi, H. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2022.naacl-main.201 · Cited by 169 · see citing works

A Systematic Assessment of Syntactic Generalization in Neural Language Models

Hu, J., Gauthier, J., Qian, P., Wilcox, E., & Levy, R. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.158 · Cited by 134 · see citing works

Language Model Behavior: A Comprehensive Survey

Chang, T. A., & Bergen, B. K. (2024) · Computational Linguistics · doi:10.1162/coli_a_00492 · Cited by 105 · see citing works

Are Natural Language Inference Models IMPPRESsive? Learning IMPlicature and PRESupposition

Jeretič, P., Warstadt, A., Bhooshan, S., & Williams, A. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.768 · Cited by 83 · see citing works

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 · doi:10.18653/v1/2023.conll-babylm.1 · Cited by 74 · see citing works

Large-scale benchmark yields no evidence that language model surprisal explains syntactic disambiguation difficultyPreprint

Huang, K., Arehalli, S., Kugemoto, M., Muxica, C., Prasad, G., Dillon, B., & Linzen, T. (2023) · Journal of Memory and Language · doi:10.1016/j.jml.2024.104510 · Cited by 70 · see citing works

Evaluating NLP Models via Contrast SetsPreprint

Gardner, M., Artzi, Y., Basmova, V., Berant, J., Bogin, B., Chen, S., Dasigi, P., Dua, D., Elazar, Y., Gottumukkala, A., et al. (2020) · arXiv (Cornell University) · Cited by 65 · see citing works

SyntaxGym: An Online Platform for Targeted Evaluation of Language Models

Gauthier, J., Hu, J., Wilcox, E., Qian, P., & Levy, R. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations · doi:10.18653/v1/2020.acl-demos.10 · Cited by 61 · see citing works

Linguistically inspired roadmap for building biologically reliable protein language models

Vu, M. H., Akbar, R., Robert, P. A., Świątczak, B., Sandve, G. K., Greiff, V., & Haug, D. T. T. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00637-1 · Cited by 57 · see citing works

Can neural networks acquire a structural bias from raw linguistic data?Preprint

Warstadt, A., & Bowman, S. R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.06761 · Cited by 45 · see citing works

The Zero Resource Speech Challenge 2021: Spoken Language Modelling

Dunbar, E., Bernard, M., Hamilakis, N., Nguyen, T. A., de Seyssel, M., Rozé, P., Rivière, M., Kharitonov, E., & Emmanuel Dupoux, A. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-1755 · Cited by 43 · see citing works

Fighting Fire with Fire: The Dual Role of LLMs in Crafting and Detecting Elusive Disinformation

Lucas, J., Uchendu, A., Yamashita, M., Lee, J., Rohatgi, S., & Lee, D. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.883 · Cited by 37 · see citing works

Large Language Models, Agency, and Why Speech Acts are Beyond Them (For Now) – A Kantian-Cum-Pragmatist Case

Gubelmann, R. (2024) · Philosophy & Technology · doi:10.1007/s13347-024-00696-1 · Cited by 36 · see citing works

On the Robustness of Language Encoders against Grammatical Errors

Yin, F., Long, Q., Meng, T., & Chang, K.-W. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.310 · Cited by 31 · see citing works

Language acquisition: do children and language models follow similar learning stages?

Evanson, L., Lakretz, Y., & King, J. R. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.773 · Cited by 27 · see citing works

Testing the limits of natural language models for predicting human language judgements

Golan, T., Siegelman, M., Kriegeskorte, N., & Baldassano, C. A. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00718-1 · Cited by 26 · see citing works

Discovering the Compositional Structure of Vector Representations with Role Learning Networks

Soulos, P., McCoy, T., Linzen, T., & Smolensky, P. (2020) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/2020.blackboxnlp-1.23 · Cited by 24 · see citing works

DEMETR: Diagnosing Evaluation Metrics for Translation

Karpinska, M., Raj, N., Thai, K., Song, Y., Gupta, A., & Iyyer, M. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.649 · Cited by 22 · see citing works

SCALa: A blueprint for computational models of language acquisition in social contextPreprint

Tsuji, S., Cristià, A., & Emmanuel Dupoux, A. (2021) · doi:10.1016/j.cognition.2021.104779 · Cited by 19 · see citing works

The Life Cycle of Knowledge in Big Language Models: A Survey

Cao, B., Lin, H., Han, X., & Sun, L. (2023) · arXiv · doi:10.48550/arxiv.2303.07616 · Cited by 18

Trained on 100 million words and still in shape: BERT meets British National Corpus

Samuel, D., Kutuzov, A., Øvrelid, L., & Velldal, E. (2023) · Findings of the Association for Computational Linguistics: EACL 2023 · doi:10.18653/v1/2023.findings-eacl.146 · Cited by 14 · see citing works

Recurrent babbling: evaluating the acquisition of grammar from limited input data

Pannitto, L., & Herbelot, A. (2020) · Conference on Computational Natural Language Learning · doi:10.18653/v1/2020.conll-1.13 · Cited by 12 · see citing works

Philosophy of cognitive science in the age of deep learning

Millière, R. (2024) · Wiley Interdisciplinary Reviews Cognitive Science · doi:10.1002/wcs.1684 · Cited by 11 · see citing works

Learning Interpretable Style Embeddings via Prompting LLMs

Patel, A., Rao, D., Kothary, A., McKeown, K., & Callison-Burch, C. (2023) · arXiv · doi:10.48550/arxiv.2305.12696 · Cited by 10 · see citing works

Finding Structure in One Child's Linguistic Experience.

Wang, W., Vong, W. K., Kim, N., & Lake, B. (2023) · Cognitive Sciences · doi:10.1111/cogs.13305 · Cited by 23 · see citing works

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 · doi:10.1016/j.jml.2025.104650 · Cited by 9 · see citing works

Overestimation of Syntactic Representation in Neural Language Models

Kodner, J., & Gupta, N. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.160 · Cited by 9 · see citing works

Neural Generative Models and the Parallel Architecture of Language: A Critical Review and Outlook

Rambelli, G., Chersoni, E., Testa, D., Blache, P., & Lenci, A. (2024) · Topics in Cognitive Science · doi:10.1111/tops.12733 · Cited by 6 · see citing works

The Perceptimatic English Benchmark for Speech Perception ModelsPreprint

Millet, J., & Dunbar, E. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.03418 · Cited by 6 · see citing works

Multilingual capabilities of GPT: A study of structural ambiguity

Yoo, M. H., Kim, J.-S., & Song, S. (2025) · PLoS ONE · doi:10.1371/journal.pone.0326943 · Cited by 5 · see citing works

Causal Transformers Perform Below Chance on Recursive Nested Constructions, Unlike Humans

Lakretz, Y., Desbordes, T., Hupkes, D., & Dehaene, S. (2021) · arXiv · doi:10.48550/arxiv.2110.07240 · Cited by 4 · see citing works

A systematic evaluation of Dutch large language models’ surprisal estimates in sentence, paragraph and book reading

Boeve, S., & Bogaerts, L. (2025) · Behavior Research Methods · doi:10.3758/s13428-025-02774-4 · Cited by 4 · see citing works

Coloring the Black Box: What Synesthesia Tells Us about Character Embeddings

Kann, K., & Monsalve‐Mercado, M. M. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-main.230 · Cited by 4 · see citing works

Rigorously assessing natural language explanations of neurons

Huang, J., Geiger, A., D’Oosterlinck, K., Wu, Z., & Potts, C. (2023) · Proceedings of the 6th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP · doi:10.18653/v1/2023.blackboxnlp-1.24 · Cited by 3 · see citing works

Meaning creation in novel noun-noun compounds: humans and language models

Chen, P., Poeppel, D., & Zuanazzi, A. (2023) · Language Cognition and Neuroscience · doi:10.1080/23273798.2023.2254865 · Cited by 3 · see citing works

A Systematic Evaluation of Dutch Large Language Models’ Surprisal Estimates in Sentence, Paragraph, and Book ReadingPreprint

Boeve, S., & Bogaerts, L. (2024) · doi:10.31219/osf.io/vqnw6 · Cited by 3 · see citing works

CLIMB – Curriculum Learning for Infant-inspired Model Building

Martinez, R. D., McGovern, H., Goriely, Z., Davis, C., Caines, A., Buttery, P., & Beinborn, L. (2023) · Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning · doi:10.18653/v1/2023.conll-babylm.10 · Cited by 2 · see citing works

Language Models Fail to Introspect About Their Knowledge of Language

Song, S., Hu, J., & Mahowald, K. (2025) · arXiv · doi:10.48550/arxiv.2503.07513 · Cited by 2 · see citing works

Elements of World Knowledge (EWOK): A cognition-inspired framework for evaluating basic world knowledge in language models

Ivanova, A. A., Sathe, A., Lipkin, B., Kumar, U., Radkani, S., Clark, T. H., Kauf, C., Hu, J., Pramod, R. T., Grand, G., et al. (2024) · arXiv · doi:10.48550/arxiv.2405.09605 · Cited by 2

tasksource: A Large Collection of NLP tasks with a Structured Dataset Preprocessing Framework

Sileo, D. (2024) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/2h4pkr685mkq · Cited by 2 · see citing works

ToddlerBERTa: Exploiting BabyBERTa for Grammar Learning and Language Understanding

Veysel Çağatan, Ö. (2023) · Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning · doi:10.18653/v1/2023.conll-babylm.14 · Cited by 1 · see citing works

Too Much Information: Keeping Training Simple for BabyLMs

Edman, L., & Bylinina, L. (2023) · Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning · doi:10.18653/v1/2023.conll-babylm.8 · Cited by 1 · see citing works

Lil-Bevo: Explorations of Strategies for Training Language Models in More Humanlike Ways

Govindarajan, V. S., Rodriguez, J. D., Bostrom, K., & Mahowald, K. (2023) · Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning · doi:10.18653/v1/2023.conll-babylm.27 · Cited by 1 · see citing works

AI linguistics, quo vadis?

Catasso, N. (2026) · AI-Linguistica Linguistic Studies on AI-Generated Texts and Discourses · doi:10.62408/ai-ling.v3i1.88 · Cited by 1 · see citing works

Toward context-based text-to-3D scene generation

Henlein, A. (2022) · doi:10.21248/gups.73448 · Cited by 1 · see citing works

The Learnability of the Wh-Island Constraint in Dutch by a Long Short-Term Memory NetworkPreprint

Suijkerbuijk, M., Frank, S. L., & de Swart, P. (2023) · doi:10.31234/osf.io/njr3z · Cited by 1 · see citing works

Artificial neural networks to analyze and simulate language acquisition in childrenPreprint

Lavechin, M. (2023) · doi:10.31234/osf.io/5p8ge

A Systematic Evaluation of Dutch Large Language Models’ Surprisal Estimates in Sentence, Paragraph, and Book ReadingPreprint

Boeve, S., & Bogaerts, L. (2025) · doi:10.31219/osf.io/vqnw6_v2

SLlama: Parameter-Efficient Language Model Architecture for Enhanced Linguistic Competence Under Strict Data Constraints

Omolaoye, V. A., Owoyele, B., & de Melo, G. (2025) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2025.emnlp-main.1198

Masked Diffusion Language Models with Frequency-Informed Training

Kosmopoulou, D., Georgiou, E., Dorovatas, V., Paraskevopoulos, G., & Potamianos, A. (2025) · BabyLM Workshop · doi:10.18653/v1/2025.babylm-main.38

Evaluating Large Language Models on Lithuanian Grammatical Cases

Jakubauskaitė, U., & Alhama, R. G. (2026) · Workshop on Language Models for Low-Resource Languages (LoResLM) · doi:10.18653/v1/2026.loreslm-1.32

Modelling non-native speech perception using automatic speech processing

Millet, J. (2022) · theses.fr (ABES) · doi:10.70675/86b4843az71c8z41d4za9dcz3db2229b1254

Beyond next-word prediction: hierarchical linguistic composition modulates LLM-brain alignment in timePreprint

Zhao, J., & Brennan, J. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.15.725490

Syntactic Priming in Few-Shot Learning: How Demonstration Structure Shapes LLM Performance

Yadla, P. (2026) · Joint Conference on Lexical and Computational Semantics · doi:10.18653/v1/2026.starsem-conference.2

Gradient Starvation in Low-Rank Language Model Pretraining

Omolaoye, V. A., & de Melo, G. (2026) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/2026.emnlp-main.360

Causal, Hybrid, and Ternary Transformers under a Restricted Data Budget

Srivastav, A. (2026) · BabyLM Workshop · doi:10.18653/2026.babylm-main.23

MultiBLiMP 1.0: A Massively Multilingual Benchmark of Linguistic Minimal Pairs

Jumelet, J., Weissweiler, L., Nivre, J., & Bisazza, A. (2026) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl.a.600 · Cited by 32 · see citing works

JBLiMP: Japanese Benchmark of Linguistic Minimal Pairs

Someya, T., & Oseki, Y. (2023) · Findings of the Association for Computational Linguistics: EACL · doi:10.18653/v1/2023.findings-eacl.117 · Cited by 9 · see citing works

TurBLiMP: A Turkish Benchmark of Linguistic Minimal Pairs

Başar, E., Padovani, F., Jumelet, J., & Bisazza, A. (2025) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2025.emnlp-main.834 · Cited by 4 · see citing works

CLiMP: A Benchmark for Chinese Language Model Evaluation

Xiang, B., Yang, C., Li, Y., Warstadt, A., & Kann, K. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-main.242 · Cited by 28 · see citing works

BLiMP-NL: A corpus of Dutch minimal pairs and acceptability judgements for language model evaluationPreprint

Suijkerbuijk, M., Prins, Z., Kloots, M. D. H., Zuidema, W. H., & Frank, S. L. (2024) · doi:10.31234/osf.io/mhjbx_v1