School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Peking University（北京大学）
Yiming Liang, Tianyu Zheng, Xinrun Du, Ge Zhang, Jiaheng Liu, Xingwei Qu, Wenqiang Zu, Xingrun Xing, Chujie Zheng, Lei Ma, Guoyin Wang, Zhaoxiang Zhang, Wenhao … 展开作者
Yiming Liang, Tianyu Zheng, Xinrun Du, Ge Zhang, Jiaheng Liu, Xingwei Qu, Wenqiang Zu, Xingrun Xing, Chujie Zheng, Lei Ma, Guoyin Wang, Zhaoxiang Zhang, Wenhao Huang, Xiang Yue, Jiajun Zhang
Instruction tuning enhances large language models (LLMs) to follow human instructions across diverse tasks, relying on high-quality datasets to guide behavior. However, these datasets, whether manually curated or synthetically generated, are often narrowly focused and misaligned with the broad distributions captured during pre-training, limiting LLM generalization and effective use of pre-trained knowledge. We propose Aligning Instruction Tuning with Pre-training (AITP), a method that bridges this gap by identifying coverage shortfalls in instruction-tuning datasets and rewriting underrepresented pre-training data into high-quality instruction-response pairs. This approach enriches dataset diversity while preserving task-specific objectives. Evaluations on three fully open LLMs across eight benchmarks demonstrate consistent performance improvements with AITP. Ablations highlight the benefits of adaptive data selection, controlled rewriting, and balanced integration, emphasizing the importance of aligning instruction tuning with pre-training distributions to unlock the full potential of LLMs.
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）
Demystifying Instruction Mixing for Fine-tuning Large Language Models
Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；LibrAI；Monash University（莫纳什大学）；University of British Columbia（不列颠哥伦比亚大学）；The University of Melbourne（墨尔本大学）
SwitchCIT: Switching for Continual Instruction Tuning
Coordinated Science Laboratory（协调科学实验室）；Department of Electrical and Computer Engineering（电气与计算机工程系）；Department of Mathematics（数学系）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
An Empirical Study of Instruction-tuning Large Language Models in Chinese
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）；APUS AiLMe Lab（APUS AiLMe实验室）
LongForm: Effective Instruction Tuning with Reverse Instructions
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）；Language Technology Lab, University of Cambridge（剑桥大学语言技术实验室）
Roles of Scaling and Instruction Tuning in Language Perception: Model vs. Human Attention
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）
Don't Half-listen: Capturing Key-part Information in Continual Instruction Tuning
School of Cyber Science and Engineering Nanjing University of Science and Technology China（南京理工大学信息科学与工程学院）；Meituan China（美团）；Zhipu.AI China（智谱AI）；Institute of Information Engineering Chinese Academy of Sciences China（中国科学院信息工程研究所）
MINT: Multimodal Instruction Tuning with Multimodal Interaction Grouping
University of California, San Diego（加州大学圣地亚哥分校）；Johns Hopkins University（约翰霍普金斯大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Massachusetts Institute of Technology（麻省理工学院）
本文提出 MINT 任务分组策略，按多模态交互类型组织指令微调任务，以学习可迁移技能并减少任务干扰，在泛化与专业化间取得更优平衡。
Select2Reason: Efficient Instruction-Tuning Data Selection for Long-CoT Reasoning
DataArc Tech Ltd.（DataArc科技有限公司）；Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州））；IDEA Research, International Digital Economy Academy（IDEA研究所，国际数字经济学院）；Hithink RoyalFlush Information Network Co., Ltd（Hithink RoyalFlush信息网络有限公司）
University of Southern California（南加州大学）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；The Pennsylvania State University（宾夕法尼亚州立大学）；University of California, Davis（加利福尼亚大学戴维斯分校）
