🧑‍🔬 Who cites Ethan Gotlieb Wilcox

570 citations of their works from 403 papers of our library (of the 140,884 whose bibliographies we read) · 42 with a page here, the most cited first · ← Ethan Gotlieb Wilcox

1. On the Opportunities and Risks of Foundation ModelsRishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al.2021 · 3,044 citations
2. Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language modelsAarohi Srivastava, Abhinav Rastogi, Abhishek Rao, et al.2022 · 1,695 citations
3. A Structural Probe for Finding Syntax in Word RepresentationsJohn Hewitt, Christopher D. Manning2019 · 1,228 citations
4. Linguistic Knowledge and Transferability of Contextual RepresentationsNelson F. Liu, Matt Gardner, Yonatan Belinkov, et al.2019 · 1,011 citations
5. Why Does Surprisal From Larger Transformer-Based Language Models Provide a Poorer Fit to Human Reading Times?Byung-Doh Oh, William Schuler2023 · 211 citations
6. The emergence of number and syntax units in LSTM language modelsYair Lakretz, German Kruszewski, Theo Desbordes, et al.2019 · 194 citations
7. Testing the Predictions of Surprisal Theory in 11 LanguagesEthan G. Wilcox, Tiago Pimentel, Clara Meister, et al.2023 · 158 citations
8. SyntaxGym: An Online Platform for Targeted Evaluation of Language ModelsJon Gauthier, Jennifer Hu, Ethan Wilcox, et al.2020 · 145 citations
9. A fine-grained comparison of pragmatic language understanding in humans and language modelsJennifer Hu, Sammy Floyd, Olessia Jouravlev, et al.2023 · 136 citations
10. Prompting is not a substitute for probability measurements in large language modelsJennifer Hu, Roger Levy2023 · 133 citations
11. NL-Augmenter: A Framework for Task-Sensitive Natural Language AugmentationKaustubh Dhole, Varun Gangal, Sebastian Gehrmann, et al.2023 · 84 citations
12. What Artificial Neural Networks Can Tell Us About Human Language AcquisitionAlex Warstadt, Samuel R. Bowman2022 · 72 citations
13. Harnessing the linguistic signal to predict scalar inferencesSebastian Schuster, Yuxing Chen, Judith Degen2020 · 54 citations
14. Expectations over Unspoken Alternatives Predict Pragmatic InferencesJennifer Hu, Roger Levy, Judith Degen, et al.2023 · 27 citations
15. How poor is the stimulus? Evaluating hierarchical generalization in neural networks trained on child-directed speechAditya Yedetore, Tal Linzen, Robert Frank, et al.2023 · 27 citations
16. A closer look at the performance of neural language models on reflexive anaphor licensingJennifer Hu, Sherry Yong Chen, Roger Levy2020 · 24 citations
17. On the Proper Treatment of Tokenization in PsycholinguisticsMario Giulianelli, Luca Malagutti, Juan Luis Gastaldi, et al.2024 · 23 citations
18. BLiMP: The Benchmark of Linguistic Minimal Pairs for EnglishAlex Warstadt, Alicia Parrish, Haokun Liu, et al.2020 · 19 citations
19. Causal Interventions Reveal Shared Structure Across English Filler–Gap ConstructionsSasha Boguraev, Christopher Potts, Kyle Mahowald2025 · 13 citations
20. The Impact of Token Granularity on the Predictive Power of Language Model SurprisalByung-Doh Oh, William Schuler2025 · 12 citations
21. Are Frequent Phrases Directly Retrieved like Idioms? An Investigation with Self-Paced Reading and Language ModelsGiulia Rambelli, Emmanuele Chersoni, Marco S. G. Senaldi, et al.2023 · 11 citations
22. Targeted Syntactic Evaluation on the Chomsky HierarchyTaiga Someya, Ryo Yoshida, Yohei Oseki2024 · 10 citations
23. Child-Directed Language Does Not Consistently Boost Syntax Learning in Language ModelsFrancesca Padovani, Jaap Jumelet, Yevgen Matusevych, et al.2025 · 9 citations
24. Can language models capture syntactic associations without surface cues? A case study of reflexive anaphor licensing in English control constructionsSoo-Hwan Lee, Sebastian Schuster2022 · 9 citations
25. Unveiling Language Competence Neurons: A Psycholinguistic Approach to Model InterpretabilityXufeng Duan, Xinyu Zhou, Bei Xiao, et al.2025 · 8 citations
26. SemEval-2022 Task 3: PreTENS — Evaluating Neural Networks on Presuppositional Semantic KnowledgeRoberto Zamparelli, Shammur Chowdhury, Dominique Brunato, et al.2022 · 8 citations
27. Clozing the Gap: Exploring Why Language Model Surprisal Outperforms Cloze SurprisalSathvik Nair, Byung-Doh Oh2026 · 1 citation
28. Human-like Fleeting Memory Improves Language Learning but Impairs Reading Time Prediction in Transformer Language ModelsAbishek Thamma, Micha Heilbron2026 · 1 citation
29. Probing for Reading TimesEleftheria Tsipidi, Samuel Kiegeland, Francesco Ignazio Re, et al.2026 · 1 citation
30. Surprisal and Metaphor Novelty Judgments: Moderate Correlations and Divergent Scaling Effects Revealed by Corpus-Based and Synthetic DatasetsOmar Momen, Emilie Sitter, Berenike Herrmann, et al.2026 · 1 citation
31. Aggregated Transformer Attention Measures Predict Reading Times Beyond SurprisalLukas Mielczarek, Laura Kallmeyer2026
32. Collocational bootstrapping: A hypothesis about the learning of subject-verb agreement in humans and neural networksClaire Hobbs, R. Thomas McCoy2026
33. Mechanistic Interpretability Meets Cognitive Linguistics: Modelling Locative Image Schemas in the Circuit FrameworkMattia Proietti, Afra Alishahi, Grzegorz Chrupała, et al.2026
34. Open-access Dataset on Acceptability Ratings of Korean Clausal Constructions by Humans and GPT ModelsGyu-Ho Shin, Soo-Hwan Lee, Chanyoung Lee2026
35. Predicting States of Understanding in Explanatory Interactions Using Cognitive Load-Related Linguistic CuesYu Wang, Olcay Türk, Angela Grimminger, et al.2026
36. Quantifying the cross-linguistic effects of syncretism on agreement attractionUtku Turk, Eva Neu2026
37. Reading Time in the Wild: An Assessment of Readability Predictors Based on Naturally-Observed Reading TimesSijbren van Vaals, Rik van Noord, Malvina Nissim2026
38. Semantic Information: A Difference That Makes a DifferenceJ. Nathanael Philipp, Max Kölbl, Michael Richter2026
39. Similar Predictions, Different Processes: A Multi-Level Comparison of Human and Multimodal LLM Language PredictionShuqi Wang, Zhenguang Cai2026
40. Toward Cognitive Alignment in Large Language Models: Integrating Linguistic Theory and Human DataWajdi Zaghouani2026
41. Word predictability estimates from language models are not robust to tokenizer vocabularyKien Nguyen, Suhas Arehalli2026
42. Assessing the Agreement Competence of Large Language ModelsAlba Táboas García, Leo Wanner2025

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