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The paper examines how language models learn action nominal constructions (ANCs) across 96 synthetic target languages with varied grammatical features. Using GPT‑2‑small trained from scratch, the study evaluates models on minimal pairs covering 34 grammatical phenomena, revealing that models mirror typological patterns in basic clause configurations but diverge in complement systems and ANC strategies. The findings suggest that natural language distributions may arise from both general learning pressures and functional demands not fully captured by current model experiments.
arXiv:2610.33433v1 Announce Type: new Abstract: Linguistic productivity, as evidenced by the use of words in novel contexts, is one of the most striking facts about human language. However, how abstr...
The study examines Urdu light verbs, which add schematic event meaning while staying lexically linked to their main verbs. Using contextual embeddings from UrduBERT, DunbaaBERT, and multilingual BERT on 1,126 sentences, the authors find significant representational separation between main and light uses across all verb–model pairs, yet main and light uses of the same lemma remain closer than mismatched pairs. In a seven‑way prediction task limited to light uses, UrduBERT achieves 0.866 accuracy and 0.852 macro‑F1, and maintains 0.782 accuracy when tested on unseen preceding forms, demonstrating generalization beyond local verb combinations.
arXiv:2606. 07479v1 Announce Type: cross Abstract: Turkish idiomatic light verb constructions (LVCs) are challenging for multiword expression processing because they often share the same surface form as fully literal verb-object combinations while functioning as a single, partially idiomatic predicate.
The study uses sparse autoencoders (SAEs) to probe how the Pythia-70M language model encodes grammatical number. By identifying and manipulating SAE‑derived features such as "plural noun" and "generate a plural verb," the authors show that removing these features forces the model to produce singular verbs, and vice versa. Similar asymmetries were observed in larger models (Gemma‑2‑2b and Llama‑3.1‑8b), indicating a privative representation of number in English.
The paper presents a framework that combines human-curated vocabularies with a few human-written exemplars to enable large language models (LLMs) to generate coherent text in extremely low-resource or unseen languages. By sampling candidate words from a Zipfian distribution and providing syntactic signals, the method achieves fluent sentence-level generation and promising paragraph-level results across languages with varying resource levels. The study evaluates output quality using lexical diversity, off-target ratio, and human fluency judgments, demonstrating that reasoning-enabled LLMs can effectively bootstrap synthetic data even when digital resources are scarce.