6 papers · 1 filter
Language Models Generalize to Human-like Word Order Preferences
Amanda Popadich, Shane Steinert-Threlkeld
A central question in language acquisition is whether linguistic biases can emerge from general learning mechanisms operating over underdetermined input. Artificial Language Learni…
Exposure is Optional: Learning Unlike Coordination in Language Models
Jiamu Luo, Shane Steinert-Threlkeld
Coordination, a fundamental linguistic structure, remains a subject of intense debate, and its exact nature continues to elude theoretical linguistics. A common view holds that onl…
Differences in Typological Alignment in Language Models' Treatment of Differential Argument Marking
Iskar Deng, Nathalia Xu, Shane Steinert-Threlkeld
Recent work has shown that language models (LMs) trained on synthetic corpora can exhibit typological preferences that resemble cross-linguistic regularities in human languages, pa…
Minimization of Boolean Complexity in In-Context Concept Learning
Leroy Z. Wang, R. Thomas McCoy, Shane Steinert-Threlkeld
What factors contribute to the relative success and corresponding difficulties of in-context learning for Large Language Models (LLMs)? Drawing on insights from the literature on h…
Targeted Multilingual Adaptation for Low-resource Language Families
C. M. Downey, Terra Blevins, Dhwani Serai +2
The "massively-multilingual" training of multilingual models is known to limit their utility in any one language, and they perform particularly poorly on low-resource languages. Ho…
Filtered Corpus Training (FiCT) Shows that Language Models can Generalize from Indirect Evidence
Abhinav Patil, Jaap Jumelet, Yu Ying Chiu +5
This paper introduces Filtered Corpus Training, a method that trains language models (LMs) on corpora with certain linguistic constructions filtered out from the training data, and…