What are the Essential Factors in Crafting Effective Long Context Multi-Hop Instruction Datasets? Insights and Best Practices
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Research Center for Social Computing and Interactive Robotics（社会计算与交互机器人研究中心）；Harbin Institute of Technology（哈尔滨工业大学）；School of Computer Science and Engineering, Central South University（中南大学计算机科学与工程学院）
Zhi Chen, Qiguang Chen, Libo Qin, Qipeng Guo, Haijun Lv, Yicheng Zou, Wanxiang Che, Hang Yan, Kai Chen, Dahua Lin
Recent advancements in large language models (LLMs) with extended context windows have significantly improved tasks such as information extraction, question answering, and complex planning scenarios. In order to achieve success in long context tasks, a large amount of work has been done to enhance the long context capabilities of the model through synthetic data. Existing methods typically utilize the Self-Instruct framework to generate instruction tuning data for better long context capability improvement. However, our preliminary experiments indicate that less than 35% of generated samples are multi-hop, and more than 40% exhibit poor quality, limiting comprehensive understanding and further research. To improve the quality of synthetic data, we propose the Multi-agent Interactive Multi-hop Generation (MIMG) framework, incorporating a Quality Verification Agent, a Single-hop Question Generation Agent, a Multiple Question Sampling Strategy, and a Multi-hop Question Merger Agent. This framework improves the data quality, with the proportion of high-quality, multi-hop, and diverse data exceeding 85%. Furthermore, we systematically investigate strategies for document selection, question merging, and validation techniques through extensive experiments across various models. Our findings show that our synthetic high-quality long-context instruction data significantly enhances model performance, even surpassing models trained on larger amounts of human-annotated data. Our code is available at: https://github.com/WowCZ/LongMIT.
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）
Toward General Instruction-Following Alignment for Retrieval-Augmented Generation
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；School of Artificial Intelligence, Beijing University of Posts and Telecommunications（北京邮电大学人工智能学院）
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）
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）
Stealthy Jailbreak Attacks on Large Language Models via Benign Data Mirroring
Harbin Institute of Technology（哈尔滨工业大学）；Central South University（中南大学）；East China Normal University（华东师范大学）；Beihang University（北京航空航天大学）；LibrAI；MBZUAI；Tsinghua University（清华大学）
MIT Computer Science and Artificial Intelligence Lab（麻省理工学院计算机科学与人工智能实验室）；CUHK Centre for Perceptual and Interactive Intelligence（香港中文大学感知与交互智能中心）；MIT Linguistics（麻省理工学院语言学系）
Ubiquitous Knowledge Processing Lab (UKP Lab)（普适知识处理实验室（UKP实验室））；Department of Computer Science（计算机科学系）；Hessian Center for AI (hessian.AI)（黑森人工智能中心（hessian.AI））；Technical University of Darmstadt（达姆施塔特工业大学）；cohere（Cohere公司）
GenSwarm: Scalable Multi-Robot Code-Policy Generation and Deployment via Language Models
Department of Artificial Intelligence, Westlake University（人工智能系，西湖大学）；School of Automation Science and Electrical Engineering, Beihang University（自动化科学与电气工程学院，北航）；Institute of Engineering and Technology, University of Groningen（工程与技术学院，格罗宁根大学）；Department of Computer Science, Technical University of Darmstadt（计算机科学系，德累斯顿技术大学）；School of Electrical and Electronic Engineering, The University of Sheffield（电子与电气工程学院，谢菲尔德大学）
