🔗 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»

1. Emergent Abilities of Large Language ModelsJason Wei, Yi Tay, Rishi Bommasani, et al.2022 · 2,229 citations
2. Large Language Models Struggle to Learn Long-Tail KnowledgeNikhil Kandpal, Haikang Deng, Adam Roberts, et al.2022 · 353 citations
3. Impact of Pretraining Term Frequencies on Few-Shot Numerical ReasoningYasaman Razeghi, Robert L Logan IV, Matt Gardner, et al.2022 · 162 citations
4. Language Model Behavior: A Comprehensive SurveyTyler A. Chang, Benjamin K. Bergen2024 · 148 citations
5. How Much Do Language Models Copy From Their Training Data? Evaluating Linguistic Novelty in Text Generation Using RAVENR. Thomas McCoy, Paul Smolensky, Tal Linzen, et al.2023 · 116 citations
6. Construction Grammar Provides Unique Insight into Neural Language ModelsLeonie Weissweiler, Taiqi He, Naoki Otani, et al.2023 · 26 citations
7. Child-Directed Language Does Not Consistently Boost Syntax Learning in Language ModelsFrancesca Padovani, Jaap Jumelet, Yevgen Matusevych, et al.2025 · 9 citations
8. How do we get there? Evaluating transformer neural networks as cognitive models for English past tense inflectionXiaomeng Ma, Lingyu Gao2022 · 2 citations
9. Can a Remedy Find a Researcher? Exploring the Development of Semantic Knowledge in Italian BabyLMsAlice Suozzi, Luca Capone, Gianluca Lebani, et al.2026
10. Collocational bootstrapping: A hypothesis about the learning of subject-verb agreement in humans and neural networksClaire Hobbs, R. Thomas McCoy2026

39 more papers of our library cite it; they have no page here yet.

This page in Markdown → https://md.research.elamur.ai/free/en/papers/wei-2021-frequency-effects-syntactic-rule-learning/cited-by/