| Challenge: | In this paper, we show that neural language models with different initialization, architecture, and training data acquire linguistic phenomena in a similar order, despite their different end performance. |
| Approach: | They propose to use mutual inductive bias to study linguistic representations implicit in NLMs. |
| Outcome: | The proposed approach shows that NLMs with different initialization, architecture, and training data acquire linguistic phenomena in a similar order, despite their different end performance. |
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| Challenge: | a recent study examined the cross-lingual transferability of neural language models . previous studies focused on their first language acquisition . |
| Approach: | They propose to pretrain bilingual LMs with a scenario similar to human L2 acquisition . they find that pretraining accelerated their linguistic generalization in L2 . |
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Understanding Learning Dynamics Of Language Models with SVCCA (N19-1)
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| Challenge: | a new study shows that neural models implicitly encode linguistic features . but no research shows how these encodings arise as the models are trained . |
| Approach: | They propose a method that compares learning across time and across models using annotated data. |
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A Distributional Perspective on Word Learning in Neural Language Models (2025.naacl-long)
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| Challenge: | Language models are increasingly being studied as models of human language learners. |
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Overestimation of Syntactic Representation in Neural Language Models (2020.acl-main)
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| Challenge: | Several testing methodologies have been developed to probe models’ syntactic representations. |
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Can Language Models Learn Typologically Implausible Languages? (2026.tacl-1)
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| Challenge: | Language models provide a naturalistic framework for studying artificial language learning . authors: typological universals and tendencies are thought to be caused by a learning bias . |
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Computational Expressivity of Neural Language Models (2024.acl-tutorials)
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| Challenge: | Language models (LMs) are at the forefront of NLP research due to their versatility across diverse tasks. |
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Language acquisition: do children and language models follow similar learning stages? (2023.findings-acl)
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| Challenge: | During language acquisition, children follow a typical sequence of learning stages, whereby they first learn to categorize phonemes before they develop their lexicon and eventually master complex syntactic structures. |
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A Systematic Assessment of Syntactic Generalization in Neural Language Models (2020.acl-main)
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Neural language models as psycholinguistic subjects: Representations of syntactic state (N19-1)
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| Challenge: | a recent study examines the extent to which neural network language models reflect incremental representations of syntactic state . we examine neural network model behavior on sentences chosen to probe specific aspects of the learned representations . |
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On Efficiently Representing Regular Languages as RNNs (2024.findings-acl)
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| Challenge: | Recent work by Hewitt et al. (2020) provides an interpretation of the empirical success of recurrent neural networks (RNNs) as language models (LMs). |
| Approach: | They generalize their construction and show that RNNs can efficiently represent a larger class of LMs than previously claimed. |
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