🔗 Who cites «Language Models Learn Rare Phenomena from Less Rare Phenomena: The Case of the Missing AANNs»

38 papers of our library cite it (of the 149,439 whose bibliographies we read) · 18 with a page here, the most cited first · ← back to «Language Models Learn Rare Phenomena from Less Rare Phenomena: The Case of the Missing AANNs»

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. Is Child-Directed Speech Effective Training Data for Language Models?Conference on Empirical Methods in Natural Language Processing (EMNLP)Steven Y. Feng, Noah D. Goodman, Michael C. Frank2024 · 18 citations
4. Do Construction Distributions Shape Formal Language Learning In German BabyLMs?Conference on Computational Natural Language Learning (CoNLL)Bastian Bunzeck, Daniel Duran, Sina Zarrieß2025 · 13 citations
5. What Goes Into a LM Acceptability Judgment? Rethinking the Impact of Frequency and LengthConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Lindia Tjuatja, Graham Neubig, Tal Linzen, et al.2025 · 12 citations
6. Can Language Models Learn Typologically Implausible Languages?Transactions of the Association for Computational Linguistics (TACL)Tianyang Xu, Tatsuki Kuribayashi, Yohei Oseki, et al.2026 · 10 citations
7. Aligned but Blind: Alignment Increases Implicit Bias by Reducing Awareness of RaceAnnual Meeting of the Association for Computational Linguistics (ACL)Lihao Sun, Chengzhi Mao, Valentin Hofmann, et al.2025 · 10 citations
8. Constructions are Revealed in Word DistributionsConference on Empirical Methods in Natural Language Processing (EMNLP)Joshua Rozner, Leonie Weissweiler, Kyle Mahowald, et al.2025 · 9 citations
9. Function Words as Statistical Cues for Language LearningAnnual Meeting of the Association for Computational Linguistics (ACL)Xiulin Yang, Heidi R. Getz, Ethan Gotlieb Wilcox2026 · 1 citation
10. Language Models Learn Constructional Semantics, Not To Mention Syntax: Investigating LM Understanding of Paired-Focus ConstructionsConference on Computational Natural Language Learning (CoNLL)Wesley Scivetti, Ethan Gotlieb Wilcox, Nathan Schneider, et al.2026 · 1 citation
11. What Exactly do Children Receive in Language Acquisition? A Case Study on CHILDES with Automated Detection of Filler-Gap DependenciesConference on Computational Natural Language Learning (CoNLL)Zhenghao Zhou, William Dai, Maya Viswanathan, et al.2026 · 1 citation
12. Collocational bootstrapping: A hypothesis about the learning of subject-verb agreement in humans and neural networksClaire Hobbs, R. Thomas McCoy2026
13. Cross-Modal Taxonomic Generalization in (Vision-) Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Tianyang Xu, Marcelo Sandoval-Castañeda, Karen Livescu, et al.2026
14. CxMP: A Linguistic Minimal-Pair Benchmark for Evaluating Constructional Understanding in Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Miyu Oba, Saku Sugawara2026
15. Heterogeneity in Formal Linguistic Competence of Language Models: Is Data the Real Bottleneck?Findings of the Association for Computational LinguisticsH S V N S Kowndinya Renduchintala, Sumit Bhatia2026
16. Metric Grammars: A usage-based grammatical formalism that supports generation, parsing and morphological innovationConference on Computational Natural Language Learning (CoNLL)Whitney Tabor, Hyosun Lee2026
17. On the Effect of Hyperparameters in Language Modeling for Computational LinguisticsAnnual Meeting of the Association for Computational Linguistics (ACL)Ruoxi Ning, Yongpeng Zhu, Qingcheng Zeng, et al.2026
18. Studying Expert-ese: Profiling and Classification of Domain-Specific Language Variation in Architecture with Traditional Machine Learning and LLMsWorkshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL)Carmen Schacht, Renate Delucchi Danhier2026

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

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/papers/misra-2024-language-models-learn-rare-phenomena/cited-by/