Targeted Syntactic Evaluation of Language Models
Open accessAbstract
We present a dataset for evaluating the grammaticality of the predictions of a language model.We automatically construct a large number of minimally different pairs of English sentences, each consisting of a grammatical and an ungrammatical sentence.The sentence pairs represent different variations of structure-sensitive phenomena: subject-verb agreement, reflexive anaphora and negative polarity items.We expect a language model to assign a higher probability to the grammatical sentence than the ungrammatical one.In an experiment using this data set, an LSTM language model performed poorly on many of the constructions.Multi-task training with a syntactic objective (CCG supertagging) improved the LSTM's accuracy, but a large gap remained between its performance and the accuracy of human participants recruited online.This suggests that there is considerable room for improvement over LSTMs in capturing syntax in a language model.
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BLiMP and other related syntactic benchmarks such as those presented in Marvin and Linzen (2018) and Gauthier et al. (2020) were specifically designed to mimic the types of tests invented by linguists and cognitive scientists to reveal syntactic competence, i.e., they are all based on minimal pair sentences.
“BLiMP, and other related syntactic benchmarks such as those presented in Marvin and Linzen (2018) and Gauthier et al. (2020), were specifically designed to mimic the types of tests invented by linguists and cognitive scientists to reveal syntactic competence-i.e., they are all based on minimal pair sentences.”
From Findings of the BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora (2023) · cites this paper as Marvin and Linzen (2018)
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“We are sympathetic to such concerns, but we also note that BLiMP, and other related syntactic benchmarks such as those presented in Marvin and Linzen (2018) and Gauthier et al. (2020), were specifically designed to mimic the types of tests invented by linguists and cognitive scientists to reveal…”
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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
Self-citationDo large language models resemble humans in language use?Preprint
Cai, Z. G., Duan, X., Haslett, D. A., Wang, S., & Pickering, M. J. (2023) · doi:10.31234/osf.io/s49qv · Cited by 90 · see citing works
Language models align with human judgments on key grammatical constructions
Hu, J., Mahowald, K., Lupyan, G., Ivanova, A. A., & Levy, R. (2024) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.2400917121 · Cited by 91 · see citing works
A Tale of a Probe and a Parser
Maudslay, R. H., Valvoda, J., Pimentel, T., Williams, A., & Cotterell, R. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.659 · Cited by 49 · see citing works
Understanding by Understanding Not: Modeling Negation in Language Models
Hosseini, A., Reddy, S., Bahdanau, D., Hjelm, R. D., Sordoni, A., & Courville, A. C. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.102 · Cited by 47 · see citing works
Interpretability and Analysis in Neural NLP
Belinkov, Y., Gehrmann, S., & Pavlick, E. (2020) · doi:10.18653/v1/2020.acl-tutorials.1 · Cited by 44 · see citing works
The emergence of number and syntax units in LSTM language modelsPreprint
Lakretz, Y., Kruszewski, G., Desbordes, T., Hupkes, D., Dehaene, S., & Baroni, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.07435 · Cited by 53 · see citing works
Frequency Effects on Syntactic Rule Learning in Transformers
Wei, J., Garrette, D., Linzen, T., & Pavlick, E. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.72 · Cited by 45 · see citing works
Self-citationCross-Linguistic Syntactic Evaluation of Word Prediction Models
Mueller, A., Nicolai, G., Petrou-Zeniou, P., Talmina, N., & Linzen, T. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.490 · Cited by 46 · see citing works
Self-citationUsing Priming to Uncover the Organization of Syntactic Representations in Neural Language Models
Prasad, G., van Schijndel, M., & Linzen, T. (2019) · Conference on Computational Natural Language Learning · doi:10.18653/v1/k19-1007 · Cited by 41 · see citing works
Self-citationLinguistic Profiling of a Neural Language Model
Miaschi, A., Brunato⋄, D., Dell’Orletta⋄, F., & Venturi⋄, G. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.65 · Cited by 42 · see citing works
Evaluating Models' Local Decision Boundaries 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) · doi:10.48550/arxiv.2004.02709 · Cited by 83 · see citing works
Computational Language Modeling and the Promise of In Silico Experimentation
Jain, S., Vo, V. A., Wehbe, L., & Huth, A. G. (2023) · Neurobiology of Language · doi:10.1162/nol_a_00101 · Cited by 37 · see citing works
What Can Linguistics and Deep Learning Contribute to Each Other? Response to Pater
Linzen, T. (2019) · Language · doi:10.1353/lan.2019.0015 · Cited by 45 · see citing works
Self-citationStudying 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) · doi:10.18653/v1/n19-1356 · Cited by 39 · see citing works
Self-citationNeuron-level Interpretation of Deep NLP Models: A Survey
Sajjad, H., Durrani, N., & Dalvi, F. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00519 · Cited by 41 · see citing works
Evaluating Saliency Methods for Neural Language Models
Ding, S., & Koehn, P. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.399 · Cited by 37 · see citing works
A-maze of Natural Stories: Comprehension and surprisal in the Maze task
Boyce, V., & Lévy, R. (2023) · Glossa Psycholinguistics · doi:10.5070/g6011190 · Cited by 33 · see citing works
Syntactic Structure Distillation Pretraining for Bidirectional Encoders
Kuncoro, A., Kong, L., Fried, D., Yogatama, D., Rimell, L., Dyer, C., & Blunsom, P. (2020) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00345 · Cited by 35 · see citing works
HateCheck: Functional Tests for Hate Speech Detection Models
Röttger, P., Vidgen, B., Nguyen, D., Waseem, Z., Margetts, H. Z., & Pierrehumbert, J. B. (2021) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2021.acl-long.4 · Cited by 235 · see citing works
What Limits Our Capacity to Process Nested Long-Range Dependencies in Sentence Comprehension?
Lakretz, Y., Dehaene, S., & King, J.-R. (2020) · Entropy · doi:10.3390/e22040446 · Cited by 31 · see citing works
Assessing BERT’s ability to learn Italian syntax: a study on null-subject and agreement phenomena
Guarasci, R., Silvestri, S., Pietro, G. D., Fujita, H., & Esposito, M. (2021) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-021-03297-4 · Cited by 33 · see citing works
Jumelet, J., Zuidema, W. H., & Hupkes, D. (2019) · Conference on Computational Natural Language Learning · doi:10.18653/v1/k19-1001 · Cited by 32 · see citing works
Klafka, J., & Ettinger, A. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.434 · Cited by 34 · see citing works
Asking without Telling: Exploring Latent Ontologies in Contextual Representations
Michael, J., Botha, J. A., & Tenney, I. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.552 · Cited by 32 · 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
RNNs can generate bounded hierarchical languages with optimal memory
Hewitt, J., Hahn, M., Ganguli, S., Liang, P., & Manning, C. D. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.156 · Cited by 30 · see citing works
Prokhorov, V., Shareghi, E., Li, Y., Pilehvar, M. T., & Collier, N. (2019) · Workshop on Neural Generation and Translation (WNGT) · doi:10.18653/v1/d19-5612 · Cited by 30 · see citing works
A Targeted Assessment of Incremental Processing in Neural Language Models and Humans
Wilcox, E., Vani, P., & Lévy, R. (2021) · doi:10.18653/v1/2021.acl-long.76 · Cited by 29 · see citing works
BLiMP: the benchmark of linguistic minimal pairs for English
Warstadt, A., Parrish, A., Liu, H., Mohananey, A., Peng, W., Wang, S.-F., & Bowman, S. R. (2020) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00321 · Cited by 28 · see citing works
Probing for the Usage of Grammatical Number
Lasri, K., Pimentel, T., Lenci, A., Poibeau, T., & Cotterell, R. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.603 · Cited by 28 · see citing works
Word Acquisition in Neural Language Models
Chang, T. A., & Bergen, B. K. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00444 · Cited by 30 · see citing works
Refining Targeted Syntactic Evaluation of Language Models
Newman, B., Ang, K.-S., Gong, J., & Hewitt, J. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.290 · Cited by 29 · see citing works
A Systematic Assessment of Syntactic Generalization in Neural Language ModelsPreprint
Hu, J., Gauthier, J., Qian, P., Wilcox, E., & Lévy, R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.03692 · Cited by 27 · 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
Self-citationHow much pretraining data do language models need to learn syntax?
Pérez-Mayos, L., Ballesteros, M., & Wanner, L. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.118 · Cited by 26 · see citing works
Monolingual and Cross-Lingual Acceptability Judgments with the Italian CoLA corpus
Trotta, D., Guarasci, R., Leonardelli, E., & Tonelli, S. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.250 · Cited by 28 · 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
On the proper role of linguistically-oriented deep net analysis in linguistic theorizingPreprint
Baroni, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.08694 · Cited by 30 · see citing works
Harnessing the linguistic signal to predict scalar inferences
Schuster, S., Chen, Y., & Degen, J. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.479 · Cited by 25 · see citing works
Structural Guidance for Transformer Language Models
Qian, P., Naseem, T., Lévy, R., & Astudillo, R. F. (2021) · doi:10.18653/v1/2021.acl-long.289 · Cited by 24 · see citing works
Analyzing Encoded Concepts in Transformer Language Models
Sajjad, H., Durrani, N., Dalvi, F., Alam, F., Khan, A., & Xu, J. (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.225 · Cited by 24 · see citing works
Quantum transfer learning for acceptability judgements
Buonaiuto, G., Guarasci, R., Minutolo, A., Pietro, G. D., & Esposito, M. (2024) · Quantum Machine Intelligence · doi:10.1007/s42484-024-00141-8 · Cited by 23 · see citing works
Do Syntactic Probes Probe Syntax? Experiments with Jabberwocky Probing
Maudslay, R. H., & Cotterell, R. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.11 · Cited by 23 · see citing works
Do RNNs learn human-like abstract word order preferences?Preprint
Futrell, R., & Lévy, R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.01866 · Cited by 22 · see citing works
Studying the Inductive Biases of RNNs with Synthetic Variations of Natural LanguagesPreprint
Ravfogel, S., Goldberg, Y., & Linzen, T. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.06400 · Cited by 22 · see citing works
Self-citation
- Authors
- Marvin, R., & Linzen, T.
- Affiliations
- Johns Hopkins University (US)
- Corresponding author
- Linzen, Tal
- ORCID
- Linzen, Tal
- Venue
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Published
- 2018
- Pages
- 1192-1202
- OpenAlex
- W2888922637
- Cited by
- 334
- References
- 46
- Citations by year
- 2026: 10 · 2025: 15 · 2024: 21 · 2023: 51 · 2022: 38 · 2021: 77 · 2020: 69 · 2019: 51 · 2018: 2
- FWCI
- 20.41
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Social Sciences › Arts and Humanities › Language and Linguistics › Syntax, Semantics, Linguistic Variation
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- grammaticality judgment, subject-verb agreement, negative polarity items, LSTM language models, human vs machine performance
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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