The paper reviews how large language models (LLMs) are employed to study infant syntax acquisition, focusing on the BabyLM challenge that aims for human‑level syntactic performance using developmentally realistic corpora. It critically examines dataset construction, model selection, training procedures, and syntactic evaluation methods, highlighting methodological assumptions that limit the theoretical reach of these studies. The authors find that using developmentally realistic corpora has only modest impact on benchmark performance, pointing to fundamental computational differences between LLMs and actual infant syntax learners.
arXiv:2609.37121v1 Announce Type: new Abstract: Cross-linguistic effects are a central topic in bilingual first-language acquisition. Artificial learners can help investigate L1-L2 interactions by en...
arXiv:2608. 13545v1 Announce Type: cross Abstract: Modern language models are trained on heterogeneous web-scale text corpora.
The study introduces a Difference in Surprisal method that uses GPT‑2 token surprisal to automatically label telicity in English CHILDES corpora, validated against expert judgments. Logistic regression classifiers trained on 12 syntactic and lexical semantic features reveal that child speech achieves near‑perfect telicity classification using a single deterministic cue—the presence of a post‑verbal determiner—whereas adult speech relies more on verb class and other lexical semantic features, with the determiner cue neutralized. This developmental trajectory supports syntactic bootstrapping, showing that learners initially exploit high‑frequency structural cues before developing fully compositional, verb‑based event structures.
The study investigates how different types of caregiver feedback influence grammar learning by training small GPT‑2‑style models on child‑directed language and fine‑tuning them with reinforcement learning. Four feedback categories—communicative, structural alignment, semantic contingency, and affective—were evaluated, with structural alignment showing the strongest improvement in grammaticality and communicative feedback yielding moderate gains. Semantic contingency and affective feedback did not enhance grammaticality, though they may aid other language learning aspects, indicating that various feedback forms contribute complementarily to language acquisition.
LuxIT is a monolingual instruction‑tuning dataset for Luxembourgish, created by synthesizing instruction‑answer pairs from native texts using the DeepSeek‑R1‑0528 model and a quality‑assurance LLM‑as‑judge process. The resulting 227,507 high‑quality pairs were used to fine‑tune 14 LLMs (≤15 B parameters), yielding an average accuracy increase of +5.37 percentage points on standardized Luxembourgish proficiency exams and improvements in macro‑averaged F1 on nine of the fourteen downstream NLP tasks. These findings demonstrate that synthetic monolingual data can effectively enhance LLM performance in low‑resource languages and reveal the complex relationship between exam performance and practical NLP gains.
arXiv:2606.05087v2 Announce Type: replace Abstract: Frequent verbs such as 'have' and 'make' can function either as collocates in light-verb constructions or as full lexical predicates, as in 'make a...
The paper investigates whether training small decoder-only transformers on code‑switched text can induce cross‑lingual alignment. Using two 100‑million‑word multilingual corpora—one a mix of English, Dutch, and Chinese BabyBabelLM data, and another generated by inserting word‑ and sentence‑level code‑switching via an LLM—the authors find that code‑switched training aligns representations of parallel text, especially across different scripts, and that this alignment persists when later training on monolingual documents. A curriculum that progresses from word‑level code‑switching to sentence‑level code‑switching and finally to monolingual data yields models that outperform baselines on the BabyLM evaluation suite, demonstrating that code‑switching curriculum learning is an effective data augmentation strategy for multilingual pretraining.
arXiv:2609.37891v1 Announce Type: cross Abstract: Current pre-training datasets are derived from web crawls, with all their issues, and were not designed to support mid- and post-training pipelines--...
arXiv:2602. 09992v2 Announce Type: replace-cross Abstract: Several recent contributions have evaluated the Poverty of the Stimulus Hypothesis (PoSH) using Artificial Neural Networks (ANNs).
The paper proposes the interlingua hypothesis, suggesting that large language models translate by encoding a source sentence into a latent, task‑agnostic feature space and then decoding a target sentence from that space. Three lines of evidence support this: (1) BLEU variance across language pairs is largely explained by language‑specific competences without pair‑specific interactions; (2) many model components influence both monolingual and translation tasks; and (3) fine‑tuning on monolingual data recovers most translation gains seen with aligned documents. These findings converge to support the hypothesis and point toward new ways to understand and improve LLM translation.
arXiv:2605. 15607v2 Announce Type: replace-cross Abstract: Large language models (LLMs) achieve high pass rates on code generation benchmarks, yet whether they can transfer this ability to languages absent from pretraining remains poorly understood.
The paper explores how large language models (LLMs) can predict typological features using an in-context learning approach with data from URIEL+ and Glottolog. Zero‑shot prompting alone is inadequate, but providing phylogenetic and geographic neighbour evidence enables LLMs to outperform all baselines, even for low‑resource languages. Additionally, most LLM rationales align with the supplied evidence, suggesting a move toward explainable typological predictions.