@inproceedings{chang-etal-2025-mind,
title = "Mind the Gap: How {B}aby{LM}s Learn Filler-Gap Dependencies",
author = "Chang, Chi-Yun and
Huang, Xueyang and
Nasir, Humaira and
Storks, Shane and
Akingbade, Olawale and
Dai, Huteng",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.761/",
pages = "15060--15076",
ISBN = "979-8-89176-332-6",
abstract = "Humans acquire syntactic constructions like filler-gap dependencies from limited and often noisy input. Can neural language models do the same? We investigate this question by evaluating GPT-2 models trained on child-oriented input from the BabyLM Challenge. Our experiments focus on whether these ``baby'' language models acquire filler-gap dependencies, generalize across constructions, and respect structural constraints such as island effects. We apply a suite of syntactic constructions to four models trained on child language, including two base models (trained on 10M and 100M tokens) and two well-performing models from the BabyLM Challenge (ConcreteGPT and BabbleGPT). We evaluate model behavior using wh-licensing scores, flip tests, and grammaticality contrasts across four constructions. Results show that BabyLM-scale models partially acquire filler-gap dependencies but often fail to generalize or fully capture island constraints."
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<abstract>Humans acquire syntactic constructions like filler-gap dependencies from limited and often noisy input. Can neural language models do the same? We investigate this question by evaluating GPT-2 models trained on child-oriented input from the BabyLM Challenge. Our experiments focus on whether these “baby” language models acquire filler-gap dependencies, generalize across constructions, and respect structural constraints such as island effects. We apply a suite of syntactic constructions to four models trained on child language, including two base models (trained on 10M and 100M tokens) and two well-performing models from the BabyLM Challenge (ConcreteGPT and BabbleGPT). We evaluate model behavior using wh-licensing scores, flip tests, and grammaticality contrasts across four constructions. Results show that BabyLM-scale models partially acquire filler-gap dependencies but often fail to generalize or fully capture island constraints.</abstract>
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%0 Conference Proceedings
%T Mind the Gap: How BabyLMs Learn Filler-Gap Dependencies
%A Chang, Chi-Yun
%A Huang, Xueyang
%A Nasir, Humaira
%A Storks, Shane
%A Akingbade, Olawale
%A Dai, Huteng
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F chang-etal-2025-mind
%X Humans acquire syntactic constructions like filler-gap dependencies from limited and often noisy input. Can neural language models do the same? We investigate this question by evaluating GPT-2 models trained on child-oriented input from the BabyLM Challenge. Our experiments focus on whether these “baby” language models acquire filler-gap dependencies, generalize across constructions, and respect structural constraints such as island effects. We apply a suite of syntactic constructions to four models trained on child language, including two base models (trained on 10M and 100M tokens) and two well-performing models from the BabyLM Challenge (ConcreteGPT and BabbleGPT). We evaluate model behavior using wh-licensing scores, flip tests, and grammaticality contrasts across four constructions. Results show that BabyLM-scale models partially acquire filler-gap dependencies but often fail to generalize or fully capture island constraints.
%U https://aclanthology.org/2025.emnlp-main.761/
%P 15060-15076
Markdown (Informal)
[Mind the Gap: How BabyLMs Learn Filler-Gap Dependencies](https://aclanthology.org/2025.emnlp-main.761/) (Chang et al., EMNLP 2025)
ACL
- Chi-Yun Chang, Xueyang Huang, Humaira Nasir, Shane Storks, Olawale Akingbade, and Huteng Dai. 2025. Mind the Gap: How BabyLMs Learn Filler-Gap Dependencies. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 15060–15076, Suzhou, China. Association for Computational Linguistics.