arXiv:2402. 18121v2 Announce Type: replace-cross Abstract: This study assesses four cutting-edge language models in the underexplored Aminoacian language.
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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...
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: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...
The paper investigates how machine‑translated English data from 24 diverse source languages influences small English language models. It finds that source language affects model behavior: lexical diversity drives overall perplexity, while grammatical performance correlates with typological similarity to English when sufficient data is used. Additionally, translation quality strongly predicts language‑modeling performance.
The paper introduces the first large‑scale benchmark dataset of Bangla idioms, along with a synthetic multiple‑choice question set for idiom meaning identification. It evaluates recent large language models on three idiom‑related tasks—paraphrasing, idiom span detection, and meaning identification—using zero‑shot and few‑shot prompting. Results show significant variability across models, with Phi‑4‑mini‑instruct best at paraphrasing, Kimi‑K2‑32b‑instruct excelling at span detection, and Gemini‑2.5‑flash leading in meaning identification.
arXiv:2604. 18347v2 Announce Type: replace-cross Abstract: Vision Language Models (VLMs) achieved rapid progress in the recent years.