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Dialogue Is Not Enough to Make a Communicative BabyLM (But Neither Is Developmentally Inspired Reinforcement Learning)
Francesca Padovani, Bastian Bunzeck, Manar Ali +4
We investigate whether pre-training exclusively on dialogue data results in formally and functionally apt small language models. Based on this pre-trained llamalogue model, we empl…
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…
NeLLCom-Lex: A Neural-agent Framework to Study the Interplay between Lexical Systems and Language Use
Yuqing Zhang, Ecesu Ürker, Tessa Verhoef +2
Lexical semantic change has primarily been investigated with observational and experimental methods; however, observational methods (corpus analysis, distributional semantic modeli…
TurBLiMP: A Turkish Benchmark of Linguistic Minimal Pairs
Ezgi Başar, Francesca Padovani, Jaap Jumelet +1
We introduce TurBLiMP, the first Turkish benchmark of linguistic minimal pairs, designed to evaluate the linguistic abilities of monolingual and multilingual language models (LMs).…
MultiBLiMP 1.0: A Massively Multilingual Benchmark of Linguistic Minimal Pairs
Jaap Jumelet, Leonie Weissweiler, Joakim Nivre +1
We introduce MultiBLiMP 1.0, a massively multilingual benchmark of linguistic minimal pairs, covering 101 languages and 2 types of subject-verb agreement, containing more than 128,…
Simulating the Emergence of Differential Case Marking with Communicating Neural-Network Agents
Yuchen Lian, Arianna Bisazza, Tessa Verhoef
Differential Case Marking (DCM) refers to the phenomenon where grammatical case marking is applied selectively based on semantic, pragmatic, or other factors. The emergence of DCM…