---
title: "Who cites Kristina Gulordava"
description: "395 papers of the Elamur Research library cite the works of Kristina Gulordava, found in their bibliographies; the most cited first."
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---
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# Who cites Kristina Gulordava

398 citations of their works from 395 papers of our library (of the 140,884 whose bibliographies we read) · 57 with a page here, the most cited first · [← Kristina Gulordava](https://md.research.elamur.ai/free/en/authors/kristina-gulordava/)

1. [**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/)
2. [**Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference**R. Thomas McCoy, Ellie Pavlick, Tal Linzen*2019 · 1,630 citations*](https://md.research.elamur.ai/free/en/papers/mccoy-2019-right-wrong-reasons-diagnosing-syntactic/)
3. [**Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey**Bonan Min, Hayley Ross, Elior Sulem, et al.*2021 · 1,548 citations*](https://md.research.elamur.ai/free/en/papers/min-2021-recent-advances-natural-language-processing/)
4. [**Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned**Elena Voita, David Talbot, Fedor Moiseev, et al.*2019 · 1,511 citations*](https://md.research.elamur.ai/free/en/papers/voita-2019-analyzing-multi-head-self-attention/)
5. [**Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change**William L. Hamilton, Jure Leskovec, Dan Jurafsky*2016 · 1,270 citations*](https://md.research.elamur.ai/free/en/papers/hamilton-2016-diachronic-word-embeddings-reveal-statistical/)
6. [**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/)
7. [**A Survey of Data Augmentation Approaches for NLP**Steven Y. Feng, Varun Gangal, Jason Wei, et al.*2021 · 935 citations*](https://md.research.elamur.ai/free/en/papers/feng-2021-survey-data-augmentation-approaches-nlp/)
8. [**Analysis Methods in Neural Language Processing: A Survey**Yonatan Belinkov, James Glass*2019 · 735 citations*](https://md.research.elamur.ai/free/en/papers/belinkov-2019-analysis-methods-neural-language-processing/)
9. [**What do you learn from context? Probing for sentence structure in contextualized word representations**Ian Tenney, Patrick Xia, Berlin Chen, et al.*2019 · 582 citations*](https://md.research.elamur.ai/free/en/papers/tenney-2019-learn-context-probing-sentence-structure/)
10. [**Temporal Analysis of Language through Neural Language Models**Yoon Kim, Yi-I Chiu, Kentaro Hanaki, et al.*2014 · 439 citations*](https://md.research.elamur.ai/free/en/papers/kim-2014-temporal-analysis-language-through-neural/)
11. [**Negated and Misprimed Probes for Pretrained Language Models: Birds Can Talk, But Cannot Fly**Nora Kassner, Hinrich Schütze*2020 · 431 citations*](https://md.research.elamur.ai/free/en/papers/kassner-2020-negated-misprimed-probes-pretrained-language/)
12. [**A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios**Michael A. Hedderich, Lukas Lange, Heike Adel, et al.*2021 · 399 citations*](https://md.research.elamur.ai/free/en/papers/hedderich-2021-survey-recent-approaches-natural-language/)
13. [**Open Sesame: Getting inside BERT's Linguistic Knowledge**Yongjie Lin, Yi Chern Tan, Robert Frank*2019 · 379 citations*](https://md.research.elamur.ai/free/en/papers/lin-2019-open-sesame-getting-inside-bert/)
14. [**An Analysis of Encoder Representations in Transformer-Based Machine Translation**Alessandro Raganato, Jörg Tiedemann*2018 · 366 citations*](https://md.research.elamur.ai/free/en/papers/raganato-2018-analysis-encoder-representations-transformer-based/)
15. [**Information-Theoretic Probing with Minimum Description Length**Elena Voita, Ivan Titov*2020 · 339 citations*](https://md.research.elamur.ai/free/en/papers/voita-2020-information-theoretic-probing-minimum-description/)
16. [**Cultural Shift or Linguistic Drift? Comparing Two Computational Measures of Semantic Change**William L. Hamilton, Jure Leskovec, Dan Jurafsky*2016 · 324 citations*](https://md.research.elamur.ai/free/en/papers/hamilton-2016-cultural-shift-or-linguistic-drift/)
17. [**Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Little**Koustuv Sinha, Robin Jia, Dieuwke Hupkes, et al.*2021 · 292 citations*](https://md.research.elamur.ai/free/en/papers/sinha-2021-masked-language-modeling-distributional-hypothesis/)
18. [**Diachronic word embeddings and semantic shifts: a survey**Andrey Kutuzov, Lilja Øvrelid, Terrence Szymanski, et al.*2018 · 282 citations*](https://md.research.elamur.ai/free/en/papers/kutuzov-2018-diachronic-word-embeddings-semantic-shifts/)
19. [**XTREME-R: Towards More Challenging and Nuanced Multilingual Evaluation**Sebastian Ruder, Noah Constant, Jan Botha, et al.*2021 · 277 citations*](https://md.research.elamur.ai/free/en/papers/ruder-2021-xtreme-r-more-challenging-nuanced/)
20. [**What do RNN Language Models Learn about Filler-Gap Dependencies?**Ethan Wilcox, Roger Levy, Takashi Morita, et al.*2018 · 276 citations*](https://md.research.elamur.ai/free/en/papers/wilcox-2018-rnn-language-models-learn-about/)
21. [**Perturbed Masking: Parameter-free Probing for Analyzing and Interpreting BERT**Zhiyong Wu, Yun Chen, Ben Kao, et al.*2020 · 244 citations*](https://md.research.elamur.ai/free/en/papers/wu-2020-perturbed-masking-parameter-free-probing/)
22. [**Analysing Lexical Semantic Change with Contextualised Word Representations**Mario Giulianelli, Marco Del Tredici, Raquel Fernández*2020 · 239 citations*](https://md.research.elamur.ai/free/en/papers/giulianelli-2020-analysing-lexical-semantic-change-contextualised/)
23. [**Outta Control: Laws of Semantic Change and Inherent Biases in Word Representation Models**Haim Dubossarsky, Daphna Weinshall, Eitan Grossman*2017 · 196 citations*](https://md.research.elamur.ai/free/en/papers/dubossarsky-2017-outta-control-laws-semantic-change/)
24. [**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/)
25. [**Word Sense Induction for Novel Sense Detection**Jey Han Lau, Paul Cook, Diana McCarthy, et al.*2012 · 187 citations*](https://md.research.elamur.ai/free/en/papers/lau-2012-word-sense-induction-novel-sense/)
26. [**Probing What Different NLP Tasks Teach Machines about Function Word Comprehension**Najoung Kim, Roma Patel, Adam Poliak, et al.*2019 · 161 citations*](https://md.research.elamur.ai/free/en/papers/kim-2019-probing-what-different-nlp-tasks/)
27. [**Diachronic Usage Relatedness (DURel): A Framework for the Annotation of Lexical Semantic Change**Dominik Schlechtweg, Sabine Schulte im Walde, Stefanie Eckmann*2018 · 158 citations*](https://md.research.elamur.ai/free/en/papers/schlechtweg-2018-diachronic-usage-relatedness-durel-framework/)
28. [**How Grammatical is Character-level Neural Machine Translation? Assessing MT Quality with Contrastive Translation Pairs**Rico Sennrich*2017 · 147 citations*](https://md.research.elamur.ai/free/en/papers/sennrich-2017-how-grammatical-is-character-level/)
29. [**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/)
30. [**BabyBERTa: Learning More Grammar With Small-Scale Child-Directed Language**Philip A. Huebner, Elior Sulem, Fisher Cynthia, et al.*2021 · 91 citations*](https://md.research.elamur.ai/free/en/papers/huebner-2021-babyberta-learning-more-grammar-small/)
31. [**Probing for idiomaticity in vector space models**Marcos Garcia, Tiago Kramer Vieira, Carolina Scarton, et al.*2021 · 89 citations*](https://md.research.elamur.ai/free/en/papers/garcia-2021-probing-idiomaticity-vector-space-models/)
32. [**RNNs as psycholinguistic subjects: Syntactic state and grammatical dependency**Richard Futrell, Ethan Wilcox, Takashi Morita, et al.*2018 · 75 citations*](https://md.research.elamur.ai/free/en/papers/futrell-2018-rnns-psycholinguistic-subjects-syntactic-state/)
33. [**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/)
34. [**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/)
35. [**CxGBERT: BERT meets Construction Grammar**Harish Tayyar Madabushi, Laurence Romain, Dagmar Divjak, et al.*2020 · 43 citations*](https://md.research.elamur.ai/free/en/papers/madabushi-2020-cxgbert-bert-meets-construction-grammar/)
36. [**Language acquisition: do children and language models follow similar learning stages?**Linnea Evanson, Yair Lakretz, Jean-Rémi King*2023 · 33 citations*](https://md.research.elamur.ai/free/en/papers/evanson-2023-language-acquisition-children-language-models/)
37. [**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/)
38. [**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/)
39. [**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/)
40. [**Universal Dependencies and Quantitative Typological Trends. A Case Study on Word Order**Chiara Alzetta, Felice Dell'Orletta, Simonetta Montemagni, et al.*2018 · 14 citations*](https://md.research.elamur.ai/free/en/papers/alzetta-2018-universal-dependencies-quantitative-typological-trends/)
41. [**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/)
42. [**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/)
43. [**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/)
44. [**Exploring diachronic syntactic shifts with dependency length: the case of scientific English**Tom S Juzek, Marie Pauline Krielke, Elke Teich*2020 · 9 citations*](https://md.research.elamur.ai/free/en/papers/juzek-2020-exploring-diachronic-syntactic-shifts-dependency/)
45. [**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/)
46. [**An Error Analysis Framework for Shallow Surface Realization**Anastasia Shimorina, Yannick Parmentier, Claire Gardent*2021 · 8 citations*](https://md.research.elamur.ai/free/en/papers/shimorina-2021-error-analysis-framework-shallow-surface/)
47. [**Syntactic Language Change in English and German: Metrics, Parsers, and Convergences**Yanran Chen, Wei Zhao, Anne Breitbarth, et al.*2024 · 5 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-syntactic-language-change-english-german/)
48. [**Inherent Dependency Displacement Bias of Transition-Based Algorithms**Mark Anderson, Carlos Gómez-Rodríguez*2020 · 5 citations*](https://md.research.elamur.ai/free/en/papers/anderson-2020-inherent-dependency-displacement-bias-transition/)
49. [**Multilingual Nonce Dependency Treebanks: Understanding how Language Models Represent and Process Syntactic Structure**David Arps, Laura Kallmeyer, Younes Samih, et al.*2024 · 4 citations*](https://md.research.elamur.ai/free/en/papers/arps-2024-multilingual-nonce-dependency-treebanks-understanding/)
50. [**Modeling the Evolution of English Noun Compounds with Feature-Rich Diachronic Compositionality Prediction**Filip Miletić, Sabine Schulte im Walde*2025 · 3 citations*](https://md.research.elamur.ai/free/en/papers/miletic-2025-modeling-evolution-english-noun-compounds/)
51. [**TafBERTa: Learning Grammatical Rules from Small-Scale Language Acquisition Data in Hebrew**Anita Gelboim, Elior Sulem*2025 · 3 citations*](https://md.research.elamur.ai/free/en/papers/gelboim-2025-tafberta-learning-grammatical-rules-small/)
52. [**Transparent Semantic Change Detection with Dependency-Based Profiles**Bach Phan Tat, Kris Heylen, Dirk Geeraerts, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/tat-2026-transparent-semantic-change-detection-dependency/)
53. [**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/)
54. [**Modeling the Memory-Surprisal Trade-Off over Time: Communicative Efficiency Decreases with Lexico-Grammatical Change in Scientific English**Julius Steuer, Marie-Pauline Krielke, Stefania Degaetano-Ortlieb, et al.*2026*](https://md.research.elamur.ai/free/en/papers/steuer-2026-modeling-memory-surprisal-trade-off/)
55. [**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/)
56. [**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/)
57. [**Detecting terminologisation in nouns (Detección de terminologización en sustantivos)**Javiera Ahumada, Rogelio Nazar*2025*](https://md.research.elamur.ai/free/en/papers/ahumada-2025-detecting-terminologisation-nouns/)

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

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