---
title: "Who cites Jon Gauthier — 436 papers of our library"
description: "436 papers of the Elamur Research library cite the works of Jon Gauthier, found in their bibliographies; the most cited first."
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---
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# Who cites Jon Gauthier

500 citations of their works from 436 papers of our library (of the 149,439 whose bibliographies we read) · 100 with a page here, the most cited first · [← Jon Gauthier](https://md.research.elamur.ai/free/en/authors/jon-gauthier/)

1. **[A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://md.research.elamur.ai/free/en/papers/williams-2018-broad-coverage-challenge-corpus-sentence/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Adina Williams, Nikita Nangia, Samuel R. Bowman*2018 · 5,551 citations*
2. [**A Structured Self-attentive Sentence Embedding arXiv:1703.03130**Zhouhan Lin, Minwei Feng, Cicero Nogueira dos Santos, et al.*2017 · 1,797 citations*](https://md.research.elamur.ai/free/en/papers/lin-2017-structured-self-attentive-sentence-embedding/)
3. [**Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models arXiv:2206.04615 · Transactions on Machine Learning Research, May/2022, https://openreview.net/forum?id=uyTL5Bvosj**Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, et al.*2022 · 1,756 citations*](https://md.research.elamur.ai/free/en/papers/srivastava-2022-imitation-game-quantifying-extrapolating-capabilities/)
4. [**Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference**R. Thomas McCoy, Ellie Pavlick, Tal Linzen*2019 · 1,662 citations*](https://md.research.elamur.ai/free/en/papers/mccoy-2019-right-wrong-reasons-diagnosing-syntactic/)
5. [**Deep Biaffine Attention for Neural Dependency Parsing**Timothy Dozat, Christopher D. Manning*2016 · 1,326 citations*](https://md.research.elamur.ai/free/en/papers/dozat-2016-deep-biaffine-attention-neural-dependency/)
6. **[Long Short-Term Memory-Networks for Machine Reading](https://md.research.elamur.ai/free/en/papers/cheng-2016-long-short-term-memory-networks/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jianpeng Cheng, Li Dong, Mirella Lapata*2016 · 1,313 citations*
7. **[DiSAN: Directional Self-Attention Network for RNN/CNN-Free Language Understanding](https://md.research.elamur.ai/free/en/papers/shen-2017-disan-directional-self-attention-network/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Tao Shen, Tianyi Zhou, Guodong Long, et al.*2017 · 854 citations*
8. [**Breaking NLI Systems with Sentences that Require Simple Lexical Inferences**Max Glockner, Vered Shwartz, Yoav Goldberg*2018 · 525 citations*](https://md.research.elamur.ai/free/en/papers/glockner-2018-breaking-nli-systems-sentences-that/)
9. [**DyNet: The Dynamic Neural Network Toolkit arXiv:1701.03980**Graham Neubig, Chris Dyer, Yoav Goldberg, et al.*2017 · 489 citations*](https://md.research.elamur.ai/free/en/papers/neubig-2017-dynet-dynamic-neural-network-toolkit/)
10. **[Experience Grounds Language](https://md.research.elamur.ai/free/en/papers/bisk-2020-experience-grounds-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yonatan Bisk, Ari Holtzman, Jesse Thomason, et al.*2020 · 439 citations*
11. **[Neural Natural Language Inference Models Enhanced with External Knowledge](https://md.research.elamur.ai/free/en/papers/chen-2018-neural-natural-language-inference-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qian Chen, Xiaodan Zhu, Zhen-Hua Ling, et al.*2018 · 315 citations*
12. **[Good-Enough Compositional Data Augmentation](https://md.research.elamur.ai/free/en/papers/andreas-2020-good-enough-compositional-data-augmentation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jacob Andreas*2020 · 313 citations*
13. **[Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Little](https://md.research.elamur.ai/free/en/papers/sinha-2021-masked-language-modeling-distributional-hypothesis/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Koustuv Sinha, Robin Jia, Dieuwke Hupkes, et al.*2021 · 297 citations*
14. [**SocialIQA: Commonsense Reasoning about Social Interactions arXiv:1904.09728**Maarten Sap, Hannah Rashkin, Derek Chen, et al.*2019 · 259 citations*](https://md.research.elamur.ai/free/en/papers/sap-2019-socialiqa-commonsense-reasoning-about-social/)
15. **[Charagram: Embedding Words and Sentences via Character n-grams](https://md.research.elamur.ai/free/en/papers/wieting-2016-charagram-embedding-words-sentences-character/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**John Wieting, Mohit Bansal, Kevin Gimpel, et al.*2016 · 242 citations*
16. **[What Happens To BERT Embeddings During Fine-tuning?](https://md.research.elamur.ai/free/en/papers/merchant-2020-what-happens-bert-embeddings-during/) [BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP](https://md.research.elamur.ai/free/en/venues/blackboxnlp/)**Amil Merchant, Elahe Rahimtoroghi, Ellie Pavlick, et al.*2020 · 220 citations*
17. **[Why Does Surprisal From Larger Transformer-Based Language Models Provide a Poorer Fit to Human Reading Times?](https://md.research.elamur.ai/free/en/papers/oh-2023-why-does-surprisal-larger-transformer/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Byung-Doh Oh, William Schuler*2023 · 211 citations*
18. [**What Artificial Neural Networks Can Tell Us About Human Language Acquisition arXiv:2208.07998**Alex Warstadt, Samuel R. Bowman*2022 · 177 citations*](https://md.research.elamur.ai/free/en/papers/warstadt-2022-artificial-neural-networks-tell-us/)
19. **[Language Models as Agent Models](https://md.research.elamur.ai/free/en/papers/andreas-2022-language-models-as-agent-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jacob Andreas*2022 · 165 citations*
20. **[BabyBERTa: Learning More Grammar With Small-Scale Child-Directed Language](https://md.research.elamur.ai/free/en/papers/huebner-2021-babyberta-learning-more-grammar-small/) [Conference on Computational Natural Language Learning (CoNLL)](https://md.research.elamur.ai/free/en/venues/conll/)**Philip A. Huebner, Elior Sulem, Fisher Cynthia, et al.*2021 · 164 citations*
21. [**Language models show human-like content effects on reasoning tasks arXiv:2207.07051**Ishita Dasgupta, Andrew K. Lampinen, Stephanie C. Y. Chan, et al.*2022 · 162 citations*](https://md.research.elamur.ai/free/en/papers/dasgupta-2022-language-models-show-human-like/)
22. **[Testing the Predictions of Surprisal Theory in 11 Languages](https://md.research.elamur.ai/free/en/papers/wilcox-2023-testing-predictions-surprisal-theory-11/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Ethan G. Wilcox, Tiago Pimentel, Clara Meister, et al.*2023 · 159 citations*
23. **[Do latent tree learning models identify meaningful structure in sentences?](https://md.research.elamur.ai/free/en/papers/williams-2018-do-latent-tree-learning-models/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Adina Williams, Andrew Drozdov, Samuel R. Bowman*2018 · 149 citations*
24. **[Language Model Behavior: A Comprehensive Survey](https://md.research.elamur.ai/free/en/papers/chang-2024-language-model-behavior-comprehensive-survey/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Tyler A. Chang, Benjamin K. Bergen*2024 · 148 citations*
25. **[SyntaxGym: An Online Platform for Targeted Evaluation of Language Models](https://md.research.elamur.ai/free/en/papers/gauthier-2020-syntaxgym-online-platform-targeted-evaluation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jon Gauthier, Jennifer Hu, Ethan Wilcox, et al.*2020 · 147 citations*
26. [**A fine-grained comparison of pragmatic language understanding in humans and language models**Jennifer Hu, Sammy Floyd, Olessia Jouravlev, et al.*2023 · 136 citations*](https://md.research.elamur.ai/free/en/papers/hu-2023-fine-grained-comparison-pragmatic-language/)
27. **[UnNatural Language Inference](https://md.research.elamur.ai/free/en/papers/sinha-2021-unnatural-language-inference/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Koustuv Sinha, Prasanna Parthasarathi, Joelle Pineau, et al.*2021 · 135 citations*
28. **[Interpretable Neural Predictions with Differentiable Binary Variables](https://md.research.elamur.ai/free/en/papers/bastings-2019-interpretable-neural-predictions-differentiable-binary/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jasmijn Bastings, Wilker Aziz, Ivan Titov*2019 · 135 citations*
29. **[Prompting is not a substitute for probability measurements in large language models](https://md.research.elamur.ai/free/en/papers/hu-2023-prompting-is-not-substitute-probability/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jennifer Hu, Roger Levy*2023 · 133 citations*
30. **[Revisiting the Uniform Information Density Hypothesis](https://md.research.elamur.ai/free/en/papers/meister-2021-revisiting-uniform-information-density-hypothesis/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Clara Meister, Tiago Pimentel, Patrick Haller, et al.*2021 · 124 citations*
31. **[Can Language Models Encode Perceptual Structure Without Grounding? A Case Study in Color](https://md.research.elamur.ai/free/en/papers/abdou-2021-can-language-models-encode-perceptual/) [Conference on Computational Natural Language Learning (CoNLL)](https://md.research.elamur.ai/free/en/venues/conll/)**Mostafa Abdou, Artur Kulmizev, Daniel Hershcovich, et al.*2021 · 122 citations*
32. **[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*
33. [**Neural Models of Factuality**Rachel Rudinger, Aaron Steven White, Benjamin Van Durme*2018 · 112 citations*](https://md.research.elamur.ai/free/en/papers/rudinger-2018-neural-models-factuality/)
34. [**minicons: Enabling Flexible Behavioral and Representational Analyses of Transformer Language Models arXiv:2203.13112**Kanishka Misra*2022 · 102 citations*](https://md.research.elamur.ai/free/en/papers/misra-2022-minicons-enabling-flexible-behavioral-representational/)
35. [**Simulating Action Dynamics with Neural Process Networks arXiv:1711.05313**Antoine Bosselut, Omer Levy, Ari Holtzman, et al.*2017 · 102 citations*](https://md.research.elamur.ai/free/en/papers/bosselut-2017-simulating-action-dynamics-neural-process/)
36. **[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*
37. **[NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation](https://md.research.elamur.ai/free/en/papers/dhole-2023-nl-augmenter-framework-task-sensitive/) [Northern European Journal of Language Technology](https://md.research.elamur.ai/free/en/venues/northern-european-journal-of-language-technology/)**Kaustubh Dhole, Varun Gangal, Sebastian Gehrmann, et al.*2023 · 86 citations*
38. **[ORCA: A Challenging Benchmark for Arabic Language Understanding](https://md.research.elamur.ai/free/en/papers/elmadany-2023-orca-challenging-benchmark-arabic-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**AbdelRahim Elmadany, ElMoatez Billah Nagoudi, Muhammad Abdul-Mageed*2023 · 83 citations*
39. **[Language models are not naysayers: An analysis of language models on negation benchmarks](https://md.research.elamur.ai/free/en/papers/truong-2023-language-models-naysayers-analysis-language/) [Joint Conference on Lexical and Computational Semantics (*SEM)](https://md.research.elamur.ai/free/en/venues/starsem/)**Thinh Hung Truong, Timothy Baldwin, Karin Verspoor, et al.*2023 · 78 citations*
40. **[Structural Persistence in Language Models: Priming as a Window into Abstract Language Representations](https://md.research.elamur.ai/free/en/papers/sinclair-2022-structural-persistence-language-models-priming/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Arabella Sinclair, Jaap Jumelet, Willem Zuidema, et al.*2022 · 74 citations*
41. **[CLiMP: A Benchmark for Chinese Language Model Evaluation](https://md.research.elamur.ai/free/en/papers/xiang-2021-climp-benchmark-chinese-language-model/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Beilei Xiang, Changbing Yang, Yu Li, et al.*2021 · 73 citations*
42. **[Transformer Grammars: Augmenting Transformer Language Models with Syntactic Inductive Biases at Scale](https://md.research.elamur.ai/free/en/papers/sartran-2022-transformer-grammars-augmenting-transformer-language/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Laurent Sartran, Samuel Barrett, Adhiguna Kuncoro, et al.*2022 · 72 citations*
43. **[Mission: Impossible Language Models](https://md.research.elamur.ai/free/en/papers/kallini-2024-mission-impossible-language-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Julie Kallini, Isabel Papadimitriou, Richard Futrell, et al.*2024 · 70 citations*
44. [**Do Neural Language Representations Learn Physical Commonsense? arXiv:1908.02899**Maxwell Forbes, Ari Holtzman, Yejin Choi*2019 · 64 citations*](https://md.research.elamur.ai/free/en/papers/forbes-2019-do-neural-language-representations-learn/)
45. **[Can Language Models Handle Recursively Nested Grammatical Structures? A Case Study on Comparing Models and Humans](https://md.research.elamur.ai/free/en/papers/lampinen-2024-can-language-models-handle-recursively/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Andrew Lampinen*2024 · 63 citations*
46. **[A Better Way to Do Masked Language Model Scoring](https://md.research.elamur.ai/free/en/papers/kauf-2023-better-way-do-masked-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Carina Kauf, Anna A. Ivanova*2023 · 53 citations*
47. **[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*
48. [**A Mention-Ranking Model for Abstract Anaphora Resolution**Ana Marasović, Leo Born, Juri Opitz, et al.*2017 · 43 citations*](https://md.research.elamur.ai/free/en/papers/marasovic-2017-mention-ranking-model-abstract-anaphora/)
49. [**Arc-swift: A Novel Transition System for Dependency Parsing**Peng Qi, Christopher D. Manning*2017 · 39 citations*](https://md.research.elamur.ai/free/en/papers/qi-2017-arc-swift-novel-transition-system/)
50. **[CausalGym: Benchmarking causal interpretability methods on linguistic tasks](https://md.research.elamur.ai/free/en/papers/arora-2024-causalgym-benchmarking-causal-interpretability-methods/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Aryaman Arora, Dan Jurafsky, Christopher Potts*2024 · 38 citations*
51. **[How to Compute the Probability of a Word](https://md.research.elamur.ai/free/en/papers/pimentel-2024-how-compute-probability-word/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Tiago Pimentel, Clara Meister*2024 · 38 citations*
52. [**BHASA: A Holistic Southeast Asian Linguistic and Cultural Evaluation Suite for Large Language Models arXiv:2309.06085**Wei Qi Leong, Jian Gang Ngui, Yosephine Susanto, et al.*2023 · 33 citations*](https://md.research.elamur.ai/free/en/papers/leong-2023-bhasa-holistic-southeast-asian-linguistic/)
53. **[Probing Contextual Language Models for Common Ground with Visual Representations](https://md.research.elamur.ai/free/en/papers/ilharco-2021-probing-contextual-language-models-common/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Gabriel Ilharco, Rowan Zellers, Ali Farhadi, et al.*2021 · 33 citations*
54. **[Large Language Models Are Human-Like Internally](https://md.research.elamur.ai/free/en/papers/kuribayashi-2025-large-language-models-are-human/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Tatsuki Kuribayashi, Yohei Oseki, Souhaib Ben Taieb, et al.*2025 · 30 citations*
55. [**Log Probabilities Are a Reliable Estimate of Semantic Plausibility in Base and Instruction-Tuned Language Models arXiv:2403.14859**Carina Kauf, Emmanuele Chersoni, Alessandro Lenci, et al.*2024 · 29 citations*](https://md.research.elamur.ai/free/en/papers/kauf-2024-log-probabilities-reliable-estimate-semantic/)
56. [**Expectations over Unspoken Alternatives Predict Pragmatic Inferences**Jennifer Hu, Roger Levy, Judith Degen, et al.*2023 · 27 citations*](https://md.research.elamur.ai/free/en/papers/hu-2023-expectations-over-unspoken-alternatives-predict/)
57. **[Sensitivity as a Complexity Measure for Sequence Classification Tasks](https://md.research.elamur.ai/free/en/papers/hahn-2021-sensitivity-as-complexity-measure-sequence/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Michael Hahn, Dan Jurafsky, Richard Futrell*2021 · 26 citations*
58. **[Filtered Corpus Training (FiCT) Shows that Language Models Can Generalize from Indirect Evidence](https://md.research.elamur.ai/free/en/papers/patil-2024-filtered-corpus-training-fict-shows/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Abhinav Patil, Jaap Jumelet, Yu Ying Chiu, et al.*2024 · 23 citations*
59. **[On the Proper Treatment of Tokenization in Psycholinguistics](https://md.research.elamur.ai/free/en/papers/giulianelli-2024-proper-treatment-tokenization-psycholinguistics/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Mario Giulianelli, Luca Malagutti, Juan Luis Gastaldi, et al.*2024 · 23 citations*
60. [**Language Models as Models of Language arXiv:2408.07144**Raphael Milliere*2024 · 21 citations*](https://md.research.elamur.ai/free/en/papers/milliere-2024-language-models-models-language/)
61. **[Predicting scalar diversity with context-driven uncertainty over alternatives](https://md.research.elamur.ai/free/en/papers/hu-2022-predicting-scalar-diversity-context-driven/) [Workshop on Cognitive Modeling and Computational Linguistics (CMCL)](https://md.research.elamur.ai/free/en/venues/cmcl/)**Jennifer Hu, Roger Levy, Sebastian Schuster*2022 · 19 citations*
62. [**Adversarial Analysis of Natural Language Inference Systems arXiv:1912.03441**Tiffany Chien, Jugal Kalita*2019 · 15 citations*](https://md.research.elamur.ai/free/en/papers/chien-2019-adversarial-analysis-natural-language-inference/)
63. [**Causal Interventions Reveal Shared Structure Across English Filler–Gap Constructions**Sasha Boguraev, Christopher Potts, Kyle Mahowald*2025 · 13 citations*](https://md.research.elamur.ai/free/en/papers/boguraev-2025-causal-interventions-reveal-shared-structure/)
64. **[Natural Language Processing RELIES on Linguistics](https://md.research.elamur.ai/free/en/papers/opitz-2025-natural-language-processing-relies-linguistics/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Juri Opitz, Shira Wein, Nathan Schneider*2025 · 13 citations*
65. **[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*
66. **[From Language to Cognition: How LLMs Outgrow the Human Language Network](https://md.research.elamur.ai/free/en/papers/alkhamissi-2025-language-cognition-how-llms-outgrow/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Badr AlKhamissi, Greta Tuckute, Yingtian Tang, et al.*2025 · 11 citations*
67. **[Are Frequent Phrases Directly Retrieved like Idioms? An Investigation with Self-Paced Reading and Language Models](https://md.research.elamur.ai/free/en/papers/rambelli-2023-are-frequent-phrases-directly-retrieved/) [Workshop on Multiword Expressions (MWE)](https://md.research.elamur.ai/free/en/venues/mwe/)**Giulia Rambelli, Emmanuele Chersoni, Marco S. G. Senaldi, et al.*2023 · 11 citations*
68. **[Can Language Models Learn Typologically Implausible Languages?](https://md.research.elamur.ai/free/en/papers/xu-2026-can-language-models-learn-typologically/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Tianyang Xu, Tatsuki Kuribayashi, Yohei Oseki, et al.*2026 · 10 citations*
69. **[Targeted Syntactic Evaluation on the Chomsky Hierarchy](https://md.research.elamur.ai/free/en/papers/someya-2024-targeted-syntactic-evaluation-chomsky-hierarchy/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Taiga Someya, Ryo Yoshida, Yohei Oseki*2024 · 10 citations*
70. [**Language Models Fail to Introspect About Their Knowledge of Language arXiv:2503.07513**Siyuan Song, Jennifer Hu, Kyle Mahowald*2025 · 9 citations*](https://md.research.elamur.ai/free/en/papers/song-2025-language-models-fail-introspect-about/)
71. [**When Your Language Model Cannot Even Do Determiners Right: Probing for Anti-Presuppositions and the Maximize Presupposition! Principle**Judith Sieker, Sina Zarrieß*2023 · 9 citations*](https://md.research.elamur.ai/free/en/papers/sieker-2023-when-your-language-model-cannot/)
72. [**Can language models capture syntactic associations without surface cues? A case study of reflexive anaphor licensing in English control constructions**Soo-Hwan Lee, Sebastian Schuster*2022 · 9 citations*](https://md.research.elamur.ai/free/en/papers/lee-2022-can-language-models-capture-syntactic/)
73. **[TurkBench: A Benchmark for Evaluating Turkish Large Language Models](https://md.research.elamur.ai/free/en/papers/toraman-2026-turkbench-benchmark-evaluating-turkish-large/) [Workshop Natural Language Processing for Turkic Languages](https://md.research.elamur.ai/free/en/venues/sigturk/)**Cagri Toraman, Ahmet Kaan Sever, Ayşe Aysu Cengiz, et al.*2026 · 8 citations*
74. [**Towards Fine-Grained Pedagogical Control over English Grammar Complexity in Educational Text Generation**Dominik Glandorf, Detmar Meurers*2024 · 7 citations*](https://md.research.elamur.ai/free/en/papers/glandorf-2024-fine-grained-pedagogical-control-over/)
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78. **[Sparks of Pure Competence in LLMs: the Case of Syntactic Center Embedding in English](https://md.research.elamur.ai/free/en/papers/hardt-2025-sparks-pure-competence-llms-case/) [Society for Computation in Linguistics (SCiL)](https://md.research.elamur.ai/free/en/venues/scil/)**Daniel Hardt*2025 · 3 citations*
79. [**Experimental Pragmatics with Machines: Testing LLM Predictions for the Inferences of Plain and Embedded Disjunctions arXiv:2405.05776**Polina Tsvilodub, Paul Marty, Sonia Ramotowska, et al.*2024 · 3 citations*](https://md.research.elamur.ai/free/en/papers/tsvilodub-2024-experimental-pragmatics-machines-testing-llm/)
80. [**For GPT-4 as with Humans: Information Structure Predicts Acceptability of Long-Distance Dependencies arXiv:2505.09005**Nicole Cuneo, Eleanor Graves, Supantho Rakshit, et al.*2025 · 2 citations*](https://md.research.elamur.ai/free/en/papers/cuneo-2025-gpt-4-humans-information-structure/)
81. [**Understanding Roles and Entities: Datasets and Models for Natural Language Inference arXiv:1904.09720**Arindam Mitra, Ishan Shrivastava, Chitta Baral*2019 · 2 citations*](https://md.research.elamur.ai/free/en/papers/mitra-2019-understanding-roles-entities-datasets-models/)
82. **[Do BabyLMs Wanna Learn Wanna Contraction? On the Learnability without Language-Specific Bias](https://md.research.elamur.ai/free/en/papers/noh-2026-do-babylms-wanna-learn-wanna/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kangsan Noh, Sanghoun Song*2026 · 1 citation*
83. **[Human-like Fleeting Memory Improves Language Learning but Impairs Reading Time Prediction in Transformer Language Models](https://md.research.elamur.ai/free/en/papers/thamma-2026-human-like-fleeting-memory-improves/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Abishek Thamma, Micha Heilbron*2026 · 1 citation*
84. **[Language Models Learn Constructional Semantics, Not To Mention Syntax: Investigating LM Understanding of Paired-Focus Constructions](https://md.research.elamur.ai/free/en/papers/scivetti-2026-language-models-learn-constructional-semantics/) [Conference on Computational Natural Language Learning (CoNLL)](https://md.research.elamur.ai/free/en/venues/conll/)**Wesley Scivetti, Ethan Gotlieb Wilcox, Nathan Schneider, et al.*2026 · 1 citation*
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98. [**Assessing the Agreement Competence of Large Language Models**Alba Táboas García, Leo Wanner*2025*](https://md.research.elamur.ai/free/en/papers/garcia-2025-assessing-agreement-competence-large-language/)
99. [**Is It JUST Semantics? A Case Study of Discourse Particle Understanding in LLMs**William Sheffield, Kanishka Misra, Valentina Pyatkin, et al.*2025*](https://md.research.elamur.ai/free/en/papers/sheffield-2025-is-it-textit-just-semantics/)
100. [**Towards an Analysis of Discourse and Interactional Pragmatic Reasoning Capabilities of Large Language Models arXiv:2408.03074**Amelie Robrecht, Judith Sieker, Clara Lachenmaier, et al.*2024*](https://md.research.elamur.ai/free/en/papers/robrecht-2024-towards-analysis-discourse-interactional-pragmatic/)

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