🔗 Who cites «How to Plant Trees in Language Models: Data and Architectural Effects on the Emergence of Syntactic Inductive Biases»

25 papers of our library cite it (of the 149,439 whose bibliographies we read) · 8 with a page here, the most cited first · ← back to «How to Plant Trees in Language Models: Data and Architectural Effects on the Emergence of Syntactic Inductive Biases»

1. Filtered Corpus Training (FiCT) Shows that Language Models Can Generalize from Indirect EvidenceTransactions of the Association for Computational Linguistics (TACL)Abhinav Patil, Jaap Jumelet, Yu Ying Chiu, et al.2024 · 23 citations
2. Language Models as Models of LanguagearXiv:2408.07144Raphael Milliere2024 · 21 citations
3. A Survey of Inductive Reasoning for Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Kedi Chen, Dezhao Ruan, Yuhao Dan, et al.2026 · 10 citations
4. Between Circuits and Chomsky: Pre-pretraining on Formal Languages Imparts Linguistic BiasesAnnual Meeting of the Association for Computational Linguistics (ACL)Michael Y. Hu, Jackson Petty, Chuan Shi, et al.2025 · 9 citations
5. Child-Directed Language Does Not Consistently Boost Syntax Learning in Language ModelsConference on Empirical Methods in Natural Language Processing (EMNLP)Francesca Padovani, Jaap Jumelet, Yevgen Matusevych, et al.2025 · 9 citations
6. TafBERTa: Learning Grammatical Rules from Small-Scale Language Acquisition Data in HebrewAnita Gelboim, Elior Sulem2025 · 3 citations
7. What did you say? Generating Child-Directed Speech Questions to Train LLMsBabyLM WorkshopWhitney Poh, Michael Tombolini, Libby Barak2025 · 3 citations
8. Child-directed speech facilitates production, not comprehension, in BabyLMsConference on Computational Natural Language Learning (CoNLL)Bastian Bunzeck, Sina Zarrieß2026

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

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