Instruction finetuning (IFT) is critical for aligning Large Language Models (LLMs) to follow instructions. While many effective IFT datasets have been introduced recently, they predominantly focus on high-resource languages like English. To better align LLMs across a broad spectrum of languages and tasks, we propose a fully synthetic, novel taxonomy (Evol) guided Multilingual, Multi-turn instruction finetuning dataset, called M2Lingual. It is constructed by first selecting a diverse set of seed examples and then utilizing the proposed Evol taxonomy to convert these seeds into complex and challenging multi-turn instructions. We demonstrate the effectiveness of M2Lingual by training LLMs of varying sizes and showcasing the enhanced performance across a diverse set of languages. We contribute the 2 step Evol taxonomy with the guided generation code: https://github.com/ServiceNow/M2Lingual, as well as the first fully synthetic, general and task-oriented, multi-turn, multilingual dataset built with Evol - M2Lingual: https://huggingface.co/datasets/ServiceNow-AI/ M2Lingual - containing 182K total IFT pairs, covering 70 languages and 17+ NLP tasks.
No Optimal Language Set Exists for Multilingual Instruction Tuning: Insights from a Linguistically-Informed Study
Wageningen University and Research（瓦赫宁根大学与研究中心）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；Koç University（科克大学）；KUIS AI Lab（KUIS人工智能实验室）
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）
Overview of the NLPCC 2025 Shared Task 4: Multi-modal, Multilingual, and Multi-hop Medical Instructional Video Question Answering Challenge
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究所，中国科学院）；School of Computer Science and Technology, Beijing Institute of Technology（计算机科学与技术学院，北京理工大学）；School of Engineering, Westlake University（工程学院，西湖大学）
We present LINGUIST, a method for generating annotated data for Intent Classification and Slot Tagging (IC+ST), via fine-tuning AlexaTM 5B, a 5-billion-parameter multilingual sequence-to-sequence (seq2seq) model, on a flexible instruction prompt. In a 10-shot novel intent setting for the SNIPS dataset, LINGUIST surpasses state-of-the-art approaches (Back-Translation and Example Extrapolation) by a wide margin, showing absolute improvement for the target intents of +1.9 points on IC Recall and +2.5 points on ST F1 Score. In the zero-shot cross-lingual setting of the mATIS++ dataset, LINGUIST ou…
Language-Aware Distillation for Multilingual Instruction-Following Speech LLMs with ASR-Only Supervision
College of Computing and Data Science（计算与数据科学学院）；Nanyang Technological University（南洋理工大学）；AI Singapore（AI新加坡）；National University of Singapore（新加坡国立大学）；Institute for Infocomm Research (I$^2$R)（信息通信研究所）；A$^{*}$STAR（A*星）
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）
BayLing 2: A Multilingual Large Language Model with Efficient Language Alignment
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Key Laboratory of Intelligent Information Processing（智能信息处理重点实验室）；Research Center of Distributed Systems（分布式系统研究中心）；Key Laboratory of AI Safety（人工智能安全重点实验室）
Investigating the Multilingual Calibration Effects of Language Model Instruction-Tuning
Mila - Quebec AI Institute（魁北克人工智能研究所）；Université de Montréal（蒙特利尔大学）；The University of Tokyo（东京大学）；Western University（西方大学）；Vector Institute（向量研究所）；Polytechnique Montréal（蒙特利尔理工学院）；CIFAR AI Chair（CIFAR人工智能主席）；AIST（日本产业技术综合研究所）
EstLLM: Enhancing Estonian Capabilities in Multilingual LLMs via Continued Pretraining and Post-Training
Institute of Computer Science, University of Tartu（塔尔图大学计算机科学研究院）；Department of Software Science, Tallinn University of Technology（塔林技术大学软件科学系）；Institute of the Estonian Language, Tallinn, Estonia（爱沙尼亚语言研究院）；School of Humanities, Tallinn University（塔林大学人文学院）
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）
Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning
Cohere For AI Community（Cohere For AI 社区）；Beijing Academy of Artificial Intelligence（北京智源人工智能研究院）；Cohere；Binghamton University（宾汉姆顿大学）；University of Limerick（利默里克大学）；IT University of Copenhagen（哥本哈根信息技术大学）；MIT（麻省理工学院）；University of Toronto（多伦多大学）；King Fahd University of Petroleum and Minerals（法赫德国王石油与矿物大学）；King Abdulaziz University（阿卜杜勒阿齐兹国王大学）；Bloomberg LP（彭博有限合伙企业）；Cohere For AI
CausalAbstain: Enhancing Multilingual LLMs with Causal Reasoning for Trustworthy Abstention
Department of Computer Science, Hong Kong Baptist University（香港 Baptist 大学计算机科学系）；School of Mathematics and Statistics, The University of Melbourne（墨尔本大学数学与统计学学院）；School of Computing and Information Systems, Singapore Management University（新加坡管理大学计算与信息系统学院）
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）
