The paper introduces SimpCue, a cue-based prompting approach for multilingual text simplification in Catalan, Spanish, and Italian. It compares three prompting strategies—baseline, gold-cue (with gold linguistic cues), and predicted-cue (with automatically predicted cues)—using the Qwen3-8B model. Results show that predicted-cue prompting yields the best overall scores across SARI, BLEU, chrF, and BERTScore, though gains over the baseline are modest and vary by language.
By Mehrzad Tareh, Horacio Saggion, Stefan Bott
The paper introduces a method for text simplification that uses a Large Language Model-as-a-Judge (LLM‑Judge) to automatically generate policy‑aligned training data, eliminating the need for costly human annotation or parallel corpora. This approach allows simplification models to adapt to various policies—such as lexical‑focused simplification or overall rewriting that trades detail for simplicity—at both sentence and document levels. Experiments show that small‑scale open‑source LLMs can match or surpass GPT‑5, with consistent improvements across model families and sizes.
By Xuanxin Wu, Yuki Arase, Masaaki Nagata
arXiv:2603.02041v2 Announce Type: replace-cross
Abstract: Large language models (LLMs) are predominantly trained on English-centric data, resulting in uneven performance for smaller languages. We stu...
By Aleksei Dorkin, Taido Purason, Emil Kalbaliyev, Hele-Andra Kuulmets, Marii Ojastu, Mark Fi\v{s}el, Tanel Alum\"ae, Eleri Aedmaa, Krister Kruusmaa, Kairit Sirts
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.
By Varun Ghat Ravikumar, Sina Ahmadi, Lena J\"ager, Rico Sennrich
Agentic benchmarks aim to measure how well AI agents plan, search, execute, and recover within realistic multi-tool environments, but they are almost exclusively in English. As AI agents are globally deployed to a linguistically diverse user base, whether agentic competence measured in English transfers to other languages remains an open question.
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...
By M\'aty\'as Osv\'ath, Enik\H{o} H\'eja, No\'emi Ligeti-Nagy
arXiv:2610.23205v1 Announce Type: new
Abstract: Large Language Models (LLMs) remain unreliable for extremely low-resource languages whose writing systems conflict with high-resource pretraining prior...
By Jingting Zheng (TJUNLP Lab, School of Computer Science and Technology, Tianjin University, China), Hong Ren (TJUNLP Lab, School of Computer Science and Technology, Tianjin University, China), Yuehan Cui (TJUNLP Lab, School of Computer Science and Technology, Tianjin University, China), Shaolin Zhu (TJUNLP Lab, School of Computer Science and Technology, Tianjin University, China), Deyi Xiong (TJUNLP Lab, School of Computer Science and Technology, Tianjin University, China)
arXiv:2510.12306v4 Announce Type: replace
Abstract: As natural language corpora expand at an unprecedented rate, manual annotation remains a significant methodological bottleneck in corpus linguistic...
By Cameron Morin, Matti Marttinen Larsson
arXiv:2606.26040v2 Announce Type: replace
Abstract: AI translation of literary works is increasingly common. While the content may be rendered adequately, we do not know enough about how readers expe...
By Yves Ferstler, Adam Podoxin, Ty Brassington, Ga\"elle Laperri\`ere, Roman Grundkiewicz, Marie-Jean Meurs, Maite Taboada, Marzena Karpinska
arXiv:2609.36214v1 Announce Type: new
Abstract: Large language models (LLMs) are increasingly used by people whose first language is not English, yet these users have been shown to receive systematic...
By Yusheng Zhou, Eleanor Lin, David Jurgens
The paper presents an empirical study of factual errors in human-written text, focusing on corrections in newspaper articles to build a taxonomy of common mistakes such as kanji misconversions and unit errors. It evaluates large language models’ ability to detect these errors, finding that even advanced models like GPT‑5.4 achieve only a 52% word‑level F1 score on synthetic data, underscoring the difficulty of the task. The work highlights the gap in research on factual error detection in human writing compared to LLM hallucinations.
By Kazuma Iwamoto, Kazumasa Omura, Shotaro Ishihara
The paper explores how Large Language Models (LLMs) such as GPT-4o-mini, Gemini-1.5-pro, and LLaMA can be used to automatically simplify complex medical texts into plain language, a process known as Plain Language Adaptation (PLA). It evaluates different prompting strategies, fine‑tunes models with QLoRA, and incorporates Mixture‑of‑Agents (MoA) techniques to improve adaptability and robustness. The results show that LLM‑driven PLA effectively makes healthcare information more comprehensible while preserving essential content.
By Ting-Wei Chang, Hen-Hsen Huang, Hsin-Hsi Chen