Instruction tuning has become a key technique for enhancing the performance of large language models, enabling them to better follow human prompts. However, low-resource languages such as Luxembourgish face severe limitations due to the lack of high-quality instruction datasets. Traditional reliance on machine translation often introduces semantic misalignment and cultural inaccuracies. In this work, we address these challenges by creating a cross-lingual instruction tuning dataset for Luxembourgish, without resorting to machine-generated translations into it. Instead, by leveraging aligned data from English, French, and German, we build a high-quality dataset that preserves linguistic and cultural nuances. We provide evidence that cross-lingual instruction tuning not only improves representational alignment across languages but also the model's generative capabilities in Luxembourgish. This highlights how cross-lingual data curation can avoid the common pitfalls of machine-translated data and directly benefit low-resource language development.
X-Instruction: Aligning Language Model in Low-resource Languages with Self-curated Cross-lingual Instructions
State Key Laboratory of Multimodal Artificial Intelligence Systems（多模态人工智能系统国家重点实验室）；Institute of Automation, CAS（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
xCoT: Cross-lingual Instruction Tuning for Cross-lingual Chain-of-Thought Reasoning
State Key Lab of Software Development Environment, Beihang University（北京航空航天大学软件开发环境国家重点实验室）；Beijing Information Science and Technology University（北京信息科技大学）；School of New Media and Communication, Tianjin University（天津大学新媒体与传播学院）
Multilingual Pretraining and Instruction Tuning Improve Cross-Lingual Knowledge Alignment, But Only Shallowly
National Key Laboratory for Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）；Department of Linguistics and Translation, City University of Hong Kong（香港城市大学语言学及翻译学系）
Think Carefully and Check Again! Meta-Generation Unlocking LLMs for Low-Resource Cross-Lingual Summarization
University of California, San Diego（加利福尼亚大学圣迭戈分校）；Nanyang Technological University（南洋理工大学）；National University of Singapore（新加坡国立大学）；Tsinghua University（清华大学）
LLMs Beyond English: Scaling the Multilingual Capability of LLMs with Cross-Lingual Feedback
School of Computation, Information and Technology, TUM（慕尼黑工业大学计算、信息与技术学院）；Center for Information and Language Processing, LMU Munich（慕尼黑大学信息与语言处理中心）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Bosch Center for Artificial Intelligence（博世人工智能中心）
Extrapolating Large Language Models to Non-English by Aligning Languages
Nanjing University（南京大学）；Peking University（北京大学）；Shanghai AI Lab（上海人工智能实验室）；The University of Hong Kong（香港大学）；University of California, Santa Barbara（加州大学圣巴巴拉分校）
Efficient Training for Cross-lingual Speech Language Models
Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences (ICT/CAS)（中国科学院计算技术研究所智能信息处理重点实验室）；State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所人工智能安全国家重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）
The Hong Kong University of Science and Technology（香港科技大学）；Google Brain（谷歌大脑）；ML Collective（机器学习集体）
General-purpose language models have demonstrated impressive capabilities, performing on par with state-of-the-art approaches on a range of downstream natural language processing (NLP) tasks and benchmarks when inferring instructions from very few examples. Here, we evaluate the multilingual skills of the GPT and T5 models in conducting multi-class classification on non-English languages without any parameter updates. We show that, given a few English examples as context, pre-trained language models can predict not only English test samples but also non-English ones. Finally, we find the in-co…
From Unaligned to Aligned: Scaling Multilingual LLMs with Multi-Way Parallel Corpora
Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；Technical University of Munich（慕尼黑技术大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Minzu University of China（民族大学）；Institute for AI, Tsinghua University（清华大学人工智能研究院）
SeaEval for Multilingual Foundation Models: From Cross-Lingual Alignment to Cultural Reasoning
Nanyang Technological University（南洋理工大学）；Institute for Infocomm Research (IR), A*STAR（新加坡科技研究局信息通信研究所）；Centre for Frontier AI Research (CFAR), A*STAR（新加坡科技研究局前沿人工智能研究中心）
Improving In-context Learning of Multilingual Generative Language Models with Cross-lingual Alignment
State Key Laboratory of Multimodal Artificial Intelligence Systems（多模态人工智能系统国家重点实验室）；Institute of Automation, CAS（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
M$^3$-Med: A Benchmark for Multi-lingual, Multi-modal, and Multi-hop Reasoning in Medical Instructional Video Understanding
Beijing Institute of Technology（北京理工大学）；The Hong Kong Polytechnic University（香港理工大学）；Chinese Academy of Sciences（中国科学院）；Air Force Hospital of Southern Theater Command of PLA（中国人民解放军南部战区空军医院）；Shenzhen Traditional Chinese Medicine Hospital（深圳中医药医院）
Multi-Agent Collaboration for Multilingual Code Instruction Tuning
Alibaba Group（阿里巴巴集团）；Shanghai Jiao Tong University（上海交通大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）
