大语言模型（LLMs）的快速演进使业界能够开发各种基于人工智能的服务。指令微调被认为是将基础模型适配到目标领域以向客户提供高质量服务的关键环节。指令微调中的一个核心挑战是获取高质量的指令数据。Self-Instruct 利用 ChatGPT API 自动生成指令数据，缓解了数据稀缺问题。为了提高指令数据质量，Self-Instruct 丢弃了大量由 ChatGPT 生成的指令，尽管由于大量无用的 API 调用，这在成本方面效率低下。为了以低成本生成高质量指令数据，我们提出了一种新颖的数据生成框架——自定向指令生成（SeDi-Instruct），该框架采用基于多样性的过滤和迭代反馈任务生成。基于多样性的过滤通过增强批次中指令的多样性，在不大量丢弃低质量生成指令的情况下保持模型准确性，从而降低了合成指令数据的成本。迭代反馈任务生成将指令生成与训练任务相结合，并利用训练过程中获得的信息来创建高质量指令集。我们的结果表明，与传统方法相比，SeDi-Instruct 将 AI 模型的准确性提升了 5.2%，同时将数据生成成本降低了 36%。
英文摘要：
The rapid evolution of Large Language Models (LLMs) has enabled the industry to develop various AI-based services. Instruction tuning is considered essential in adapting foundation models for target domains to provide high-quality services to customers. A key challenge in instruction tuning is obtaining high-quality instruction data. Self-Instruct, which automatically generates instruction data using ChatGPT APIs, alleviates the data scarcity problem. To improve the quality of instruction data, Self-Instruct discards many of the instructions generated from ChatGPT, even though it is inefficient in terms of cost owing to many useless API calls. To generate high-quality instruction data at a low cost, we propose a novel data generation framework, Self-Direct Instruction generation (SeDi-Instruct), which employs diversity-based filtering and iterative feedback task generation. Diversity-based filtering maintains model accuracy without excessively discarding low-quality generated instructions by enhancing the diversity of instructions in a batch. This reduces the cost of synthesizing instruction data. The iterative feedback task generation integrates instruction generation and training tasks and utilizes information obtained during the training to create high-quality instruction sets. Our results show that SeDi-Instruct enhances the accuracy of AI models by 5.2%, compared with traditional methods, while reducing data generation costs by 36%.
TAG-INSTRUCT: Controlled Instruction Complexity Enhancement through Structure-based Augmentation
Peking University（北京大学）；Southern University of Science and Technology（南方科技大学）；Tongji University（同济大学）；Shanghai University of Finance and Economics（上海财经大学）；The University of Hong Kong（香港大学）
MoDS: Model-oriented Data Selection for Instruction Tuning
Institute of Automation, Chinese Academy of Sciences（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）；Wuhan AI Research（武汉人工智能研究院）
RAISE: Reinforced Adaptive Instruction Selection For Large Language Models
Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Peng Cheng Laboratory（鹏城实验室）；University of Illinois Chicago（伊利诺伊大学香槟分校）；Seoul National University（首尔国立大学）；Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)（广东省人工智能与数字经济实验室（深圳））
Building Instruction-Tuning Datasets from Human-Written Instructions with Open-Weight Large Language Models
Department of Computer Science, School of Computing, Institute of Science Tokyo（计算机科学系、计算学院、科学东京研究所）；National Institute of Advanced Industrial Science and Technology（国家先进工业科学与技术研究院）；Research and Development Center for Large Language Models, NII（大型语言模型研究与开发中心、日本科学振兴会）
What Makes for Good Visual Instructions? Synthesizing Complex Visual Reasoning Instructions for Visual Instruction Tuning
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 Information, Renmin University of China（中国人民大学信息学院）；Meituan Group（美团集团）
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）
MUFFIN: Curating Multi-Faceted Instructions for Improving Instruction-Following
The Pennsylvania State University（宾夕法尼亚州立大学）；The Ohio State University（俄亥俄州立大学）；Fudan University（复旦大学）；Westlake University（西湖大学）；Temple University（天普大学）
AFD-INSTRUCTION: A Comprehensive Antibody Instruction Dataset with Functional Annotations for LLM-Based Understanding and Design
National Institute for Data Science in Health and Medicine（健康医学数据科学国家研究院）；Institute of Artificial Intelligence（人工智能研究院）；School of Life Sciences（生命科学学院）；School of Informatics（信息学院）；State Key Laboratory of Vaccines for Infectious Diseases（传染病疫苗国家重点实验室）；Xiang An Biomedicine Laboratory（翔安生物医学实验室）
ImpRIF: Stronger Implicit Reasoning Leads to Better Complex Instruction Following
ByteDance China（字节跳动中国）；School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）；State Key Laboratory of Virtual Reality Technology and Systems, Beihang University（北京航空航天大学虚拟现实技术与系统国家重点实验室）
