---
title: "Tal Linzen — all works — Elamur Research"
description: "All 36 works of Tal Linzen in the Elamur Research library, the most cited first: our summaries where we have them, else the original."
canonical: "https://research.elamur.ai/free/en/authors/tal-linzen/works/"
---
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# Tal Linzen — all works

36 works · [← Tal Linzen](https://md.research.elamur.ai/free/en/authors/tal-linzen/)

1. [**Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference**R. Thomas McCoy, Ellie Pavlick, Tal Linzen*English · 1,662 citations*](https://md.research.elamur.ai/free/en/papers/mccoy-2019-right-wrong-reasons-diagnosing-syntactic/)
2. **[Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies](https://md.research.elamur.ai/free/en/papers/linzen-2016-assessing-ability-lstms-learn-syntax/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Tal Linzen, Emmanuel Dupoux, Yoav Goldberg*2016 · 1,256 citations*
3. **[Colorless Green Recurrent Networks Dream Hierarchically](https://md.research.elamur.ai/free/en/papers/gulordava-2018-colorless-green-recurrent-networks-dream/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Kristina Gulordava, Piotr Bojanowski, Edouard Grave, et al.*2018 · 765 citations*
4. **[Targeted Syntactic Evaluation of Language Models ↗](https://aclanthology.org/D18-1151/) Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · no summary yet**Rebecca Marvin, Tal Linzen*2018 · 641 citations*
5. **[COGS: A Compositional Generalization Challenge Based on Semantic Interpretation ↗](https://aclanthology.org/2020.emnlp-main.731/) Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · no summary yet**Najoung Kim, Tal Linzen*2020 · 328 citations*
6. **[How Can We Accelerate Progress Towards Human-like Linguistic Generalization?](https://md.research.elamur.ai/free/en/papers/linzen-2020-how-can-we-accelerate-progress/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tal Linzen*2020 · 263 citations*
7. **[Findings of the BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora ↗](https://aclanthology.org/2023.conll-babylm.1/) Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning · no summary yet**Alex Warstadt et al.*2023 · 232 citations*
8. **[Syntactic Data Augmentation Increases Robustness to Inference Heuristics](https://md.research.elamur.ai/free/en/papers/min-2020-syntactic-data-augmentation-increases-robustness/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Junghyun Min, R. Thomas McCoy, Dipanjan Das, et al.*2020 · 220 citations*
9. **[Issues in evaluating semantic spaces using word analogies ↗](https://aclanthology.org/W16-2503/) Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for NLP · no summary yet**Linzen, Tal*2016 · 198 citations*
10. **[BERTs of a feather do not generalize together: Large variability in generalization across models with similar test set performance ↗](https://aclanthology.org/2020.blackboxnlp-1.21/) Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP · no summary yet**R. Thomas McCoy, Junghyun Min, Tal Linzen*2020 · 181 citations*
11. **[Probing What Different NLP Tasks Teach Machines about Function Word Comprehension](https://md.research.elamur.ai/free/en/papers/kim-2019-probing-what-different-nlp-tasks/) [Joint Conference on Lexical and Computational Semantics (*SEM)](https://md.research.elamur.ai/free/en/venues/starsem/)**Najoung Kim, Roma Patel, Adam Poliak, et al.*2019 · 163 citations*
12. **[Does Syntax Need to Grow on Trees? Sources of Hierarchical Inductive Bias in Sequence-to-Sequence Networks](https://md.research.elamur.ai/free/en/papers/mccoy-2020-does-syntax-need-grow-trees/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**R. Thomas McCoy, Robert Frank, Tal Linzen*2020 · 123 citations*
13. **[How Much Do Language Models Copy From Their Training Data? Evaluating Linguistic Novelty in Text Generation Using RAVEN](https://md.research.elamur.ai/free/en/papers/mccoy-2023-how-much-do-language-models/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**R. Thomas McCoy, Paul Smolensky, Tal Linzen, et al.*2023 · 117 citations*
14. **[Quantity doesn't buy quality syntax with neural language models ↗](https://aclanthology.org/D19-1592/) Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · no summary yet**Marten van Schijndel, Aaron Mueller, Tal Linzen*2019 · 110 citations*
15. **[Evaluating Attribution in Dialogue Systems: The BEGIN Benchmark](https://md.research.elamur.ai/free/en/papers/dziri-2022-evaluating-attribution-dialogue-systems-begin/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Nouha Dziri, Hannah Rashkin, Tal Linzen, et al.*2022 · 100 citations*
16. **[Cross-Linguistic Syntactic Evaluation of Word Prediction Models](https://md.research.elamur.ai/free/en/papers/mueller-2020-cross-linguistic-syntactic-evaluation-word/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Aaron Mueller, Garrett Nicolai, Panayiota Petrou-Zeniou, et al.*2020 · 98 citations*
17. **[Frequency Effects on Syntactic Rule Learning in Transformers](https://md.research.elamur.ai/free/en/papers/wei-2021-frequency-effects-syntactic-rule-learning/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jason Wei, Dan Garrette, Tal Linzen, et al.*2021 · 92 citations*
18. **[Counterfactual Interventions Reveal the Causal Effect of Relative Clause Representations on Agreement Prediction ↗](https://aclanthology.org/2021.conll-1.15/) Proceedings of the 25th Conference on Computational Natural Language Learning · no summary yet**Shauli Ravfogel et al.*2021 · 89 citations*
19. **[Studying the Inductive Biases of RNNs with Synthetic Variations of Natural Languages](https://md.research.elamur.ai/free/en/papers/ravfogel-2019-studying-inductive-biases-rnns-synthetic/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Shauli Ravfogel, Yoav Goldberg, Tal Linzen*2019 · 84 citations*
20. **[Improving Compositional Generalization with Latent Structure and Data Augmentation](https://md.research.elamur.ai/free/en/papers/qiu-2022-improving-compositional-generalization-latent-structure/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Linlu Qiu, Peter Shaw, Panupong Pasupat, et al.*2022 · 76 citations*
21. **[The reliability of acceptability judgments across languages ↗](https://doi.org/10.5334/gjgl.528) Glossa: a journal of general linguistics · no summary yet**Tal Linzen, Yohei Oseki*2018 · 70 citations*
22. **[The MultiBERTs: BERT Reproductions for Robustness Analysis ↗](https://arxiv.org/abs/2106.16163) arXiv:2106.16163 · no summary yet**Thibault Sellam et al.*2021 · 57 citations*
23. **[How poor is the stimulus? Evaluating hierarchical generalization in neural networks trained on child-directed speech](https://md.research.elamur.ai/free/en/papers/yedetore-2023-how-poor-is-stimulus-evaluating/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Aditya Yedetore, Tal Linzen, Robert Frank, et al.*2023 · 49 citations*
24. **[Does Putting a Linguist in the Loop Improve NLU Data Collection? ↗](https://aclanthology.org/2021.findings-emnlp.421/) Findings of the Association for Computational Linguistics: EMNLP 2021 · no summary yet**Alicia Parrish et al.*2021 · 49 citations*
25. [**NOPE: A Corpus of Naturally-Occurring Presuppositions in English**Alicia Parrish, Sebastian Schuster, Alex Warstadt, et al.*English · 40 citations*](https://md.research.elamur.ai/free/en/papers/parrish-2021-nope-corpus-naturally-occurring-presuppositions/)
26. **[How to Plant Trees in Language Models: Data and Architectural Effects on the Emergence of Syntactic Inductive Biases ↗](https://arxiv.org/abs/2305.19905) arXiv:2305.19905 · no summary yet**Aaron Mueller, Tal Linzen*2023 · 36 citations*
27. **[Can You Learn Semantics Through Next-Word Prediction? The Case of Entailment ↗](https://aclanthology.org/2024.findings-acl.161/) Findings of the Association for Computational Linguistics: ACL 2024 · no summary yet**William Merrill et al.*2024 · 13 citations*
28. **[Neural Networks Can Learn Patterns of Island-insensitivity in Norwegian ↗](https://aclanthology.org/2023.scil-1.15/) Proceedings of the Society for Computation in Linguistics 2023 · no summary yet**Anastasia Kobzeva et al.*2023 · 13 citations*
29. [**Non-entailed subsequences as a challenge for natural language inference arXiv:1811.12112**R. Thomas McCoy, Tal Linzen*2018 · 13 citations*](https://md.research.elamur.ai/free/en/papers/mccoy-2018-non-entailed-subsequences-challenge-natural/)
30. **[What Goes Into a LM Acceptability Judgment? Rethinking the Impact of Frequency and Length](https://md.research.elamur.ai/free/en/papers/tjuatja-2025-what-goes-into-lm-acceptability/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Lindia Tjuatja, Graham Neubig, Tal Linzen, et al.*2025 · 12 citations*
31. **[Between Circuits and Chomsky: Pre-pretraining on Formal Languages Imparts Linguistic Biases](https://md.research.elamur.ai/free/en/papers/hu-2025-between-circuits-chomsky-pre-pretraining/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Michael Y. Hu, Jackson Petty, Chuan Shi, et al.*2025 · 9 citations*
32. [**Manipulating language models' training data to study syntactic constraint learning: the case of English passivization arXiv:2407.04593**Cara Su-Yi Leong, Tal Linzen*2024 · 8 citations*](https://md.research.elamur.ai/free/en/papers/leong-2024-manipulating-language-models-training-data/)
33. **[Evaluating In-Context Translation with Synchronous Context-Free Grammar Transduction ↗](https://arxiv.org/abs/2604.07320) arXiv:2604.07320 · no summary yet**Jackson Petty, Jaulie Goe, Tal Linzen*2026 · 1 citation*
34. [**LLM surprisal is necessary but not sufficient to capture English garden-path effects: Evidence from joint latent modeling of reading paradigms arXiv:2602.04489**Dario Paape, Tal Linzen, Shravan Vasishth*2026*](https://md.research.elamur.ai/free/en/papers/paape-2026-llm-surprisal-necessary-but-sufficient/)
35. **[RELIC: Evaluating Complex Reasoning via the Recognition of Languages In-Context](https://md.research.elamur.ai/free/en/papers/petty-2026-relic-evaluating-complex-reasoning-recognition/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Jackson Petty, Michael Y. Hu, Wentao Wang, et al.*2026*
36. **[Always Learning, Always Mixing: Efficient and Simple Data Mixing All The Time ↗](https://arxiv.org/abs/2605.15220) arXiv (Cornell University) · no summary yet**Michael Y. Hu et al.*2026*