Author

Najoung Kim

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#natural language process... Preprint Aug 2026

Does Episodic Memory Help Close the Lexical Frequency Gap in Sensitivity to Syntactic Contrasts? A Test Using Retrieval-Augmented Language Models

Grammatical knowledge and how it is empirically tested are typically considered robust to the frequency of the lexical items in the expressions. However, neural network-based models of grammaticality exhibit high sensitivity to lexical frequency. We draw upon Complementary Learning Systems theory to test the hypothesis...

Jing Liu, Najoung Kim · 0 citations

#natural language process... Preprint Sep 2026

LLMs Learn Better In-Context from Rules than from Examples

It is found that models generally learn more reliably from rules than from examples alone, and additional examples on top of rules or simply scaling up the number of examples do not lead to consistent and significant gains.

Xiang Fu, Seungmin Cho, Yukyung Lee et al. · 0 citations

Jul 2026

Linear representations of grammaticality in neural language models

The results provide evidence that grammaticality is robustly encoded in sentence representations of a wide range of pretrained NLMs, yielding clear representational separation on the dimension of grammaticality that cannot be fully explained by alternative sentence-level factors.

Jane Li, Najoung Kim · arXiv.org · 0 citations