---
title: "Who cites Ethan Gotlieb Wilcox"
description: "403 papers of the Elamur Research library cite the works of Ethan Gotlieb Wilcox, found in their bibliographies; the most cited first."
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---
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# 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](https://md.research.elamur.ai/free/en/authors/ethan-gotlieb-wilcox/)

1. [**On the Opportunities and Risks of Foundation Models**Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al.*2021 · 3,044 citations*](https://md.research.elamur.ai/free/en/papers/bommasani-2021-opportunities-risks-foundation-models/)
2. [**Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models**Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, et al.*2022 · 1,695 citations*](https://md.research.elamur.ai/free/en/papers/srivastava-2022-imitation-game-quantifying-extrapolating-capabilities/)
3. [**A Structural Probe for Finding Syntax in Word Representations**John Hewitt, Christopher D. Manning*2019 · 1,228 citations*](https://md.research.elamur.ai/free/en/papers/hewitt-2019-structural-probe-finding-syntax-word/)
4. [**Linguistic Knowledge and Transferability of Contextual Representations**Nelson F. Liu, Matt Gardner, Yonatan Belinkov, et al.*2019 · 1,011 citations*](https://md.research.elamur.ai/free/en/papers/liu-2019-linguistic-knowledge-transferability-contextual-representations/)
5. [**Why Does Surprisal From Larger Transformer-Based Language Models Provide a Poorer Fit to Human Reading Times?**Byung-Doh Oh, William Schuler*2023 · 211 citations*](https://md.research.elamur.ai/free/en/papers/oh-2023-why-does-surprisal-larger-transformer/)
6. [**The emergence of number and syntax units in LSTM language models**Yair Lakretz, German Kruszewski, Theo Desbordes, et al.*2019 · 194 citations*](https://md.research.elamur.ai/free/en/papers/lakretz-2019-emergence-number-syntax-units-lstm/)
7. [**Testing the Predictions of Surprisal Theory in 11 Languages**Ethan G. Wilcox, Tiago Pimentel, Clara Meister, et al.*2023 · 158 citations*](https://md.research.elamur.ai/free/en/papers/wilcox-2023-testing-predictions-surprisal-theory-11/)
8. [**SyntaxGym: An Online Platform for Targeted Evaluation of Language Models**Jon Gauthier, Jennifer Hu, Ethan Wilcox, et al.*2020 · 145 citations*](https://md.research.elamur.ai/free/en/papers/gauthier-2020-syntaxgym-online-platform-targeted-evaluation/)
9. [**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/)
10. [**Prompting is not a substitute for probability measurements in large language models**Jennifer Hu, Roger Levy*2023 · 133 citations*](https://md.research.elamur.ai/free/en/papers/hu-2023-prompting-is-not-substitute-probability/)
11. [**NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation**Kaustubh Dhole, Varun Gangal, Sebastian Gehrmann, et al.*2023 · 84 citations*](https://md.research.elamur.ai/free/en/papers/dhole-2023-nl-augmenter-framework-task-sensitive/)
12. [**What Artificial Neural Networks Can Tell Us About Human Language Acquisition**Alex Warstadt, Samuel R. Bowman*2022 · 72 citations*](https://md.research.elamur.ai/free/en/papers/warstadt-2022-artificial-neural-networks-tell-us/)
13. [**Harnessing the linguistic signal to predict scalar inferences**Sebastian Schuster, Yuxing Chen, Judith Degen*2020 · 54 citations*](https://md.research.elamur.ai/free/en/papers/schuster-2020-harnessing-linguistic-signal-predict-scalar/)
14. [**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/)
15. [**How poor is the stimulus? Evaluating hierarchical generalization in neural networks trained on child-directed speech**Aditya Yedetore, Tal Linzen, Robert Frank, et al.*2023 · 27 citations*](https://md.research.elamur.ai/free/en/papers/yedetore-2023-how-poor-is-stimulus-evaluating/)
16. [**A closer look at the performance of neural language models on reflexive anaphor licensing**Jennifer Hu, Sherry Yong Chen, Roger Levy*2020 · 24 citations*](https://md.research.elamur.ai/free/en/papers/hu-2020-closer-look-performance-neural-language/)
17. [**On the Proper Treatment of Tokenization in Psycholinguistics**Mario Giulianelli, Luca Malagutti, Juan Luis Gastaldi, et al.*2024 · 23 citations*](https://md.research.elamur.ai/free/en/papers/giulianelli-2024-proper-treatment-tokenization-psycholinguistics/)
18. [**BLiMP: The Benchmark of Linguistic Minimal Pairs for English**Alex Warstadt, Alicia Parrish, Haokun Liu, et al.*2020 · 19 citations*](https://md.research.elamur.ai/free/en/papers/warstadt-2020-blimp-benchmark-linguistic-minimal-pairs/)
19. [**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/)
20. [**The Impact of Token Granularity on the Predictive Power of Language Model Surprisal**Byung-Doh Oh, William Schuler*2025 · 12 citations*](https://md.research.elamur.ai/free/en/papers/oh-2025-impact-token-granularity-predictive-power/)
21. [**Are Frequent Phrases Directly Retrieved like Idioms? An Investigation with Self-Paced Reading and Language Models**Giulia Rambelli, Emmanuele Chersoni, Marco S. G. Senaldi, et al.*2023 · 11 citations*](https://md.research.elamur.ai/free/en/papers/rambelli-2023-are-frequent-phrases-directly-retrieved/)
22. [**Targeted Syntactic Evaluation on the Chomsky Hierarchy**Taiga Someya, Ryo Yoshida, Yohei Oseki*2024 · 10 citations*](https://md.research.elamur.ai/free/en/papers/someya-2024-targeted-syntactic-evaluation-chomsky-hierarchy/)
23. [**Child-Directed Language Does Not Consistently Boost Syntax Learning in Language Models**Francesca Padovani, Jaap Jumelet, Yevgen Matusevych, et al.*2025 · 9 citations*](https://md.research.elamur.ai/free/en/papers/padovani-2025-child-directed-language-does-not/)
24. [**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/)
25. [**Unveiling Language Competence Neurons: A Psycholinguistic Approach to Model Interpretability**Xufeng Duan, Xinyu Zhou, Bei Xiao, et al.*2025 · 8 citations*](https://md.research.elamur.ai/free/en/papers/duan-2025-unveiling-language-competence-neurons-psycholinguistic/)
26. [**SemEval-2022 Task 3: PreTENS — Evaluating Neural Networks on Presuppositional Semantic Knowledge**Roberto Zamparelli, Shammur Chowdhury, Dominique Brunato, et al.*2022 · 8 citations*](https://md.research.elamur.ai/free/en/papers/zamparelli-2022-semeval-2022-task-3-pretens/)
27. [**Clozing the Gap: Exploring Why Language Model Surprisal Outperforms Cloze Surprisal**Sathvik Nair, Byung-Doh Oh*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/nair-2026-clozing-gap-exploring-why-language/)
28. [**Human-like Fleeting Memory Improves Language Learning but Impairs Reading Time Prediction in Transformer Language Models**Abishek Thamma, Micha Heilbron*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/thamma-2026-human-like-fleeting-memory-improves/)
29. [**Probing for Reading Times**Eleftheria Tsipidi, Samuel Kiegeland, Francesco Ignazio Re, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/tsipidi-2026-probing-reading-times/)
30. [**Surprisal and Metaphor Novelty Judgments: Moderate Correlations and Divergent Scaling Effects Revealed by Corpus-Based and Synthetic Datasets**Omar Momen, Emilie Sitter, Berenike Herrmann, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/momen-2026-surprisal-metaphor-novelty-judgments-moderate/)
31. [**Aggregated Transformer Attention Measures Predict Reading Times Beyond Surprisal**Lukas Mielczarek, Laura Kallmeyer*2026*](https://md.research.elamur.ai/free/en/papers/mielczarek-2026-aggregated-transformer-attention-measures-predict/)
32. [**Collocational bootstrapping: A hypothesis about the learning of subject-verb agreement in humans and neural networks**Claire Hobbs, R. Thomas McCoy*2026*](https://md.research.elamur.ai/free/en/papers/hobbs-2026-collocational-bootstrapping-hypothesis-about-learning/)
33. [**Mechanistic Interpretability Meets Cognitive Linguistics: Modelling Locative Image Schemas in the Circuit Framework**Mattia Proietti, Afra Alishahi, Grzegorz Chrupała, et al.*2026*](https://md.research.elamur.ai/free/en/papers/proietti-2026-mechanistic-interpretability-meets-cognitive-linguistics/)
34. [**Open-access Dataset on Acceptability Ratings of Korean Clausal Constructions by Humans and GPT Models**Gyu-Ho Shin, Soo-Hwan Lee, Chanyoung Lee*2026*](https://md.research.elamur.ai/free/en/papers/shin-2026-open-access-dataset-acceptability-ratings/)
35. [**Predicting States of Understanding in Explanatory Interactions Using Cognitive Load-Related Linguistic Cues**Yu Wang, Olcay Türk, Angela Grimminger, et al.*2026*](https://md.research.elamur.ai/free/en/papers/wang-2026-predicting-states-understanding-explanatory-interactions/)
36. [**Quantifying the cross-linguistic effects of syncretism on agreement attraction**Utku Turk, Eva Neu*2026*](https://md.research.elamur.ai/free/en/papers/turk-2026-quantifying-cross-linguistic-effects-syncretism/)
37. [**Reading Time in the Wild: An Assessment of Readability Predictors Based on Naturally-Observed Reading Times**Sijbren van Vaals, Rik van Noord, Malvina Nissim*2026*](https://md.research.elamur.ai/free/en/papers/vaals-2026-reading-time-wild-assessment-readability/)
38. [**Semantic Information: A Difference That Makes a Difference**J. Nathanael Philipp, Max Kölbl, Michael Richter*2026*](https://md.research.elamur.ai/free/en/papers/philipp-2026-semantic-information-difference-that-makes/)
39. [**Similar Predictions, Different Processes: A Multi-Level Comparison of Human and Multimodal LLM Language Prediction**Shuqi Wang, Zhenguang Cai*2026*](https://md.research.elamur.ai/free/en/papers/wang-2026-similar-predictions-different-processes-multi/)
40. [**Toward Cognitive Alignment in Large Language Models: Integrating Linguistic Theory and Human Data**Wajdi Zaghouani*2026*](https://md.research.elamur.ai/free/en/papers/zaghouani-2026-cognitive-alignment-large-language-models/)
41. [**Word predictability estimates from language models are not robust to tokenizer vocabulary**Kien Nguyen, Suhas Arehalli*2026*](https://md.research.elamur.ai/free/en/papers/nguyen-2026-word-predictability-estimates-language-models/)
42. [**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/)

361 more citing papers of our library have no page here yet.

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