3 citations · 3 across the 10 of their papers we have counts for
12 papers
Beyond Static and Linear: What Attention Constraints Best Fit Human Reading Times?
Lanni Bu, Xiulin Yang, Christian Clark +2
Transformer-based language models are widely used as models of human language processing, yet their attention mechanisms allow lossless access to the full preceding context, unlike…
On the use of foundation models in cognitive science
Raj Sanjay Shah, Alex Warstadt, Michael Frank +1
A host of recent studies have evaluated the cognitive and developmental alignment of Foundation Models (FMs). These investigations include evaluations of their correspondence to ad…
Dual Alignment Between Language Model Layers and Human Sentence Processing
Tatsuki Kuribayashi, Alex Warstadt, Yohei Oseki +1
A recent study (Kuribayashi et al., 2025) has shown that human sentence processing behavior, typically measured on syntactically unchallenging constructions, can be effectively mod…
BabyLM Turns 4 and Goes Multilingual: Call for Papers for the 2026 BabyLM Workshop
Leshem Choshen, Ryan Cotterell, Mustafa Omer Gul +7
The goal of the BabyLM is to stimulate new research connections between cognitive modeling and language model pretraining. We invite contributions in this vein to the BabyLM Worksh…
BabyBabelLM: A Multilingual Benchmark of Developmentally Plausible Training Data
Jaap Jumelet, Abdellah Fourtassi, Akari Haga +23
We present BabyBabelLM, a multilingual collection of datasets modeling the language a person observes from birth until they acquire a native language. We curate developmentally pla…
The Harmonic Structure of Information Contours
Eleftheria Tsipidi, Samuel Kiegeland, Franz Nowak +5
The uniform information density (UID) hypothesis proposes that speakers aim to distribute information evenly throughout a text, balancing production effort and listener comprehensi…
Towards Developmentally Plausible Rewards: Communicative Success as a Learning Signal for Interactive Language Models
Lennart Stöpler, Rufat Asadli, Mitja Nikolaus +2
We propose a method for training language models in an interactive setting inspired by child language acquisition. In our setting, a speaker attempts to communicate some informatio…
BabyLM Turns 3: Call for papers for the 2025 BabyLM workshop
Lucas Charpentier, Leshem Choshen, Ryan Cotterell +11
BabyLM aims to dissolve the boundaries between cognitive modeling and language modeling. We call for both workshop papers and for researchers to join the 3rd BabyLM competition. As…
Can Language Models Learn Typologically Implausible Languages?
Tianyang Xu, Tatsuki Kuribayashi, Yohei Oseki +2
Grammatical features across human languages show intriguing correlations often attributed to learning biases in humans. However, empirical evidence has been limited to experiments…
A Distributional Perspective on Word Learning in Neural Language Models
Filippo Ficarra, Ryan Cotterell, Alex Warstadt
Language models (LMs) are increasingly being studied as models of human language learners. Due to the nascency of the field, it is not well-established whether LMs exhibit similar…
Findings of the Second BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora
Michael Y. Hu, Aaron Mueller, Candace Ross +7
The BabyLM Challenge is a community effort to close the data-efficiency gap between human and computational language learners. Participants compete to optimize language model train…
Acquiring Linguistic Knowledge from Multimodal Input
Theodor Amariucai, Alex Warstadt
In contrast to children, language models (LMs) exhibit considerably inferior data efficiency when acquiring language. In this submission to the BabyLM Challenge (Warstadt et al., 2…