--- title: "Who cites Frequency Effects on Syntactic — 49 citing papers" description: "49 papers of the Elamur Research library cite «Frequency Effects on Syntactic Rule Learning in Transformers», found in their bibliographies…" canonical: "https://research.elamur.ai/free/en/papers/wei-2021-frequency-effects-syntactic-rule-learning/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/wei-2021-frequency-effects-syntactic-rule-learning/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Frequency Effects on Syntactic Rule Learning in…](https://md.research.elamur.ai/free/en/papers/wei-2021-frequency-effects-syntactic-rule-learning/) / cited by # Who cites «Frequency Effects on Syntactic Rule Learning in Transformers» 49 papers of our library cite it (of the 141,840 whose bibliographies we read) · 10 with a page here, the most cited first · [← back to «Frequency Effects on Syntactic Rule Learning in Transformers»](https://md.research.elamur.ai/free/en/papers/wei-2021-frequency-effects-syntactic-rule-learning/) 1. [**Emergent Abilities of Large Language Models arXiv:2206.07682**Jason Wei, Yi Tay, Rishi Bommasani, et al.*2022 · 2,229 citations*](https://md.research.elamur.ai/free/en/papers/wei-2022-emergent-abilities-large-language-models/) 2. [**Large Language Models Struggle to Learn Long-Tail Knowledge arXiv:2211.08411**Nikhil Kandpal, Haikang Deng, Adam Roberts, et al.*2022 · 353 citations*](https://md.research.elamur.ai/free/en/papers/kandpal-2022-large-language-models-struggle-learn/) **[Impact of Pretraining Term Frequencies on Few-Shot Numerical Reasoning](https://md.research.elamur.ai/free/en/papers/razeghi-2022-impact-pretraining-term-frequencies-few/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yasaman Razeghi, Robert L Logan IV, Matt Gardner, et al.*2022 · 162 citations* **[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* **[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 · 116 citations* **[Construction Grammar Provides Unique Insight into Neural Language Models](https://md.research.elamur.ai/free/en/papers/weissweiler-2023-construction-grammar-provides-unique-insight/) [International Workshop on Construction Grammars and NLP](https://md.research.elamur.ai/free/en/venues/cxgsnlp/)**Leonie Weissweiler, Taiqi He, Naoki Otani, et al.*2023 · 26 citations* **[Child-Directed Language Does Not Consistently Boost Syntax Learning in Language Models](https://md.research.elamur.ai/free/en/papers/padovani-2025-child-directed-language-does-not/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Francesca Padovani, Jaap Jumelet, Yevgen Matusevych, et al.*2025 · 9 citations* [**How do we get there? Evaluating transformer neural networks as cognitive models for English past tense inflection**Xiaomeng Ma, Lingyu Gao*2022 · 2 citations*](https://md.research.elamur.ai/free/en/papers/ma-2022-how-do-we-get-there/) **[Can a Remedy Find a Researcher? Exploring the Development of Semantic Knowledge in Italian BabyLMs](https://md.research.elamur.ai/free/en/papers/suozzi-2026-can-remedy-find-researcher-exploring/) [Joint Conference on Lexical and Computational Semantics (*SEM)](https://md.research.elamur.ai/free/en/venues/starsem/)**Alice Suozzi, Luca Capone, Gianluca Lebani, et al.*2026* [**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/) 39 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/wei-2021-frequency-effects-syntactic-rule-learning/) — Frequency Effects on Syntactic Rule Learning in Transformers