arXiv:2610.35029v1 Announce Type: new Abstract: Synthetic data has become a common resource for training language models, and this trend has spilled over into research on developmentally plausible la...
The study examines how the geometric placement of synthetic data affects discourse‑pragmatic function classification. Using 410 annotated instances of the word "look" from the British National Corpus, synthetic examples were generated with Llama 3.1 and grouped by cosine distance from real data in RoBERTa space. Six training conditions were compared, showing that examples close to real data (NEAR) yield the largest macro‑F gain, while a distance‑balanced mix gives the highest accuracy, yet none improve AUC.
The paper presents a large‑scale empirical study of 284 interpretable linguistic features to detect AI‑generated English text across 30 language models and 10 text domains. It finds that classifiers using only linguistic features can reliably distinguish machine‑from human‑written text, but most indicators are highly context‑dependent. Only lexical richness measures consistently generalize across model families and domains, offering robust signals for detection.
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...
arXiv:2606. 04177v1 Announce Type: cross Abstract: Interpretable linguistic features offer a promising approach for explaining why a given text appears machine-generated, particularly for non-expert users.
The study examines how the geometric placement of synthetic data affects discourse-pragmatic function classification. Using 410 annotated instances of the word "look" and synthetic examples generated by Llama 3.1, the authors partitioned the synthetic data by cosine distance in RoBERTa space and compared six training conditions. While all augmentation conditions improved macro‑F and accuracy over a real‑only baseline, the nearest synthetic examples (NEAR) yielded the largest macro‑F gain (0.113) and a distance‑balanced mix achieved the highest accuracy (0.748), though none improved AUC.
arXiv:2606.18389v2 Announce Type: replace Abstract: Large language models (LLMs) have become an effective tool for synthetic data generation, including for low-resource languages, where generated dat...
The study investigates whether pretrained transformer models encode functional words—such as pronouns and adverbs—in a way that mirrors human usage. By comparing embeddings of nouns with those of their functional counterparts in both isolated and parallel sentences, the authors find that functional words occupy a central yet distinct position in embedding space and that parallel lexicalized and functional sentences reside in different subspaces. Experiments show that only a mixed training set of functional and lexicalized sentences reveals shared syntactic and semantic structure, whereas training on either type alone fails to capture this parallelism.
The paper presents a pipeline that leverages large language models to extract grammatical rules, example sentences, and lexicons from descriptive grammar books, producing synthetic parallel corpora for fine‑tuning machine translation models. Evaluated on three low‑resource languages—Kalamang, Tuatschin, and Mandan—the synthetic data improves translation quality over seed‑data baselines in 75% of configurations for Kalamang and 59% for Tuatschin, achieving up to +8.8 ChrF++ gains. A factorial study across 96 configurations identifies which combinations of target part‑of‑speech, retrieval granularity, and sample volume drive performance gains and where they fail, demonstrating that static linguistic documentation can be repurposed for practical translation tools for severely under‑resourced languages.
arXiv:2608. 06589v1 Announce Type: cross Abstract: While large language model outputs are frequently analysed as a collective super variety termed "AI language," this chapter argues that this perspective coexists with distinct, model-specific linguistic signatures akin to human idiolects.
The study evaluates eleven autoregressive transformer models on English agreement attraction scenarios using a surprisal-based approach. Results show that while transformers match human reading times for prepositional phrase configurations, they perform poorly on object‑extracted relative clauses, with predictions diverging across models and failing to capture human interference patterns. The authors argue that current transformers cannot adequately model human morphosyntactic processing and call for more rigorous, comprehensive testing to avoid misleading conclusions from limited syntactic setups.
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.