arXiv:2606. 30815v1 Announce Type: cross Abstract: Recent work suggests that transformer language models show a bias towards human languages over unnatural ("impossible") languages argued to be unacquirable by humans.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
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.
FreqBLiMP is a frequency‑controlled extension of the BLiMP minimal‑pair benchmark that regenerates all 67 paradigms under explicit Zipf‑frequency regimes while preserving grammatical contrasts. The study evaluates multiple open‑weight LLM families and finds that lower lexical frequency consistently reduces sentence likelihood, yet overall contrastive acceptability accuracy drops only modestly. However, the stability in aggregate accuracy hides significant variability across linguistic phenomena, with models remaining robust on overt morphosyntactic generalization but degrading on lemma‑specific tasks.
The paper investigates how adjectival modifiers affect the semantic plausibility of events, using the Adept benchmark of 16,000 English sentence pairs that differ by a single adjective. Experiments show that sentence transformers, despite being conceptually suited to the task, underperform compared to models like RoBERTa. The authors provide an error analysis and discuss the implications of their findings for future work on balancing training and test data.
arXiv:2601.19926v3 Announce Type: replace-cross Abstract: We present a systematic review of 337 articles evaluating the syntactic abilities of Transformer-based language models (TLMs), reporting on o...
arXiv:2609.18284v1 Announce Type: new Abstract: In recent years, three initiatives have emerged to develop generative language models in Hungary. The motivation behind them is the same. For Hungarian...
Coordination, a fundamental linguistic structure, remains a subject of intense debate, and its exact nature continues to elude theoretical linguistics. A common view holds that only same-category constituents can be conjoined, which has been challenged by the many grammatical unlike coordinations found in natural language.