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.
By Michael Christlambert Sinanta (Independent Researcher), Muhammad Dehan Al Kautsar (Mohamed bin Zayed University of Artificial Intelligence), Amanda Nurul Izzah (Independent Researcher), Thanh-Nhi Nguyen (Independent Researcher), Phan Phuong Mai Chau (Mohamed bin Zayed University of Artificial Intelligence), Zheng Wei Lim (University of Amsterdam), Fajri Koto (Mohamed bin Zayed University of Artificial Intelligence)
The paper proposes using multilingual sentiment lexicons to steer language models for sentiment control, especially in low-resource languages. It finds that aggregated multilingual lexicons outperform English-derived steering vectors for mid- and low-resource languages, while monolingual lexicons are still best for languages the model was pre-trained on. The study also explains how representational density, pre-training priors, and architectural biases affect steering effectiveness, noting that large English vectors can cause linguistic collapse in low-resource settings but this is mitigated by norm-dampening in multilingual aggregation.
By Lucinda Laurent, Muhammad Dehan Al Kautsar, Alfan Farizki Wicaksono, Fajri Koto
arXiv:2607. 17524v1 Announce Type: cross Abstract: We propose Token-Level Off-Policy Labeling (TOPL), an off-policy training paradigm that reframes post-training as a token-level correctness prediction task.
By Zitong Huang, Gustavo Lucas Carvalho, Deqing Fu, Robin Jia
arXiv:2609.35868v1 Announce Type: new
Abstract: Is human readability necessary for effective fine-tuning of large language models? We investigate whether model-conditioned training representations ca...
By Jinhao Zhang, Zeyu Liu, Zicheng Yan, Yunquan Zhang, Daning Cheng, Song Tang
We propose Token-Level Off-Policy Labeling (TOPL), an off-policy training paradigm that reframes post-training as a token-level correctness prediction task. Our key intuition is that by training the model to distinguish good and bad tokens in a response, we naturally guide the model towards generating good tokens, while avoiding the pitfalls that come with directly training the model to generate off-policy tokens.
arXiv:2604. 13977v2 Announce Type: replace-cross Abstract: Synthetic data is a standard component in training large language models, yet systematic comparisons across design dimensions, including rephrasing strategy, generator model, and source data, remain absent.
By Joel Niklaus, Atsuki Yamaguchi, Michal \v{S}tef\'anik, Guilherme Penedo, Hynek Kydl\'i\v{c}ek, Elie Bakouch, Lewis Tunstall, Edward Emanuel Beeching, Thibaud Frere, Colin Raffel, Leandro von Werra, Thomas Wolf
arXiv:2609.16592v1 Announce Type: new
Abstract: This paper presents an end-to-end approach for generating context-specific large language model (LLM) benchmark datasets by combining expert input with...
By Kimberly Le Truong, Nari Johnson, Anna Kawakami, Hoda Heidari
arXiv:2601.16390v2 Announce Type: replace-cross
Abstract: Large language models exhibit strong multilingual capabilities, yet significant performance gaps persist between dominant and non-dominant la...
By Rhitabrat Pokharel, Ameeta Agrawal, Tanay Nagar
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
SharP is a text‑generation framework that uses both hard and soft prompts to guide large language models in creating task‑specific synthetic news samples for low‑resource fake news detection. The method combines structured factual constraints with learned semantic patterns, and employs a boundary‑aware strategy that focuses on classifier‑uncertain areas to refine decision boundaries. Dual objective optimization balances semantic alignment with source data and classifier feedback, aiming to improve robustness and generalization when labeled data are scarce.
By Ziyu Luo, Wei Li, Haoping Huang, Kecheng Liu, Min Gao
arXiv:2609.37891v1 Announce Type: cross
Abstract: Current pre-training datasets are derived from web crawls, with all their issues, and were not designed to support mid- and post-training pipelines--...
By Pierre-Carl Langlais, Pieter Delobelle, Yannick Detrois, Pavel Chizhov, Carlos Rosas-Hinostroza, Neil Si Smail, Benjamin Burtin, Hanna Shcharbakova, Ivan Yamshchikov, Anastasia Stasenko
arXiv:2604. 26170v2 Announce Type: replace Abstract: Adapting large language models (LLMs) to a targeted task efficiently and effectively remains a fundamental challenge.
By Ting-Wei Li, Sirui Chen, Jiaru Zou, Yingbing Huang, Tianxin Wei, Jingrui He, Hanghang Tong