Instruction tuning has emerged to enhance the capabilities of large language models (LLMs) to comprehend instructions and generate appropriate responses. Existing methods either manually annotate or employ LLM (e.g., GPT-series) to generate data for instruction tuning. However, they often overlook associating instructions with existing annotated datasets. In this paper, we propose Dynosaur, a dynamic growth paradigm for the automatic curation of instruction-tuning data. Based on the metadata of existing datasets, we use LLMs to automatically construct instruction-tuning data by identifying relevant data fields and generating appropriate instructions.
By leveraging the existing annotated datasets, Dynosaur offers several advantages: 1) it reduces the API cost for generating instructions (e.g., it costs less than $12 USD by calling GPT-3.5-turbo for generating 800K instruction tuning samples; 2) it provides high-quality data for instruction tuning (e.g., it performs better than Alpaca and Flan on Super-NI and Longform with comparable data sizes); and 3) it supports the continuous improvement of models by generating instruction-tuning data when a new annotated dataset becomes available. We further investigate a continual learning scheme for learning with the ever-growing instruction-tuning dataset, and demonstrate that replaying tasks with diverse instruction embeddings not only helps mitigate forgetting issues but generalizes to unseen tasks better.
Code and data are available at https://github.com/WadeYin9712/Dynosaur.
Uncovering Intrinsic Capabilities: A Paradigm for Data Curation in Vision-Language Models
Harbin Institute of Technology, Shenzhen, China（哈尔滨工业大学（深圳））；Hong Kong Baptist University, China（香港 Baptist大学）；City University of Hong Kong, China（香港城市大学）
Rethinking the Instruction Quality: LIFT is What You Need
Shanghai Jiao Tong University（上海交通大学）；Microsoft Cloud and AI（微软云与人工智能）；Jilin University（吉林大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
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）
Virginia Tech（弗吉尼亚理工大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；University of California, Berkeley（加州大学伯克利分校）
Rethinking Data Curation in LLM Training: Online Reweighting Offers Better Generalization than Offline Methods
University of Cambridge（剑桥大学）；OATML, University of Oxford（牛津大学OATML实验室）；University of Toronto（多伦多大学）；Shanghai Jiao Tong University（上海交通大学）；Samsung AI Center（三星人工智能中心）
With the rapid development of large language models in recent years, there has been an increasing demand for domain-specific Agents that can cater to the unique needs of enterprises and organizations. Unlike general models, which strive for broad coverage, these specialized Agents rely on focused datasets tailored to their intended applications. This research proposes a pipeline that leverages the power of LLMs and the Retrieval-Augmented Generation related framework to construct high-quality instruction datasets for fine-tuning on specific domains using custom document collections. By ingesti…
VisIT-Bench: A Benchmark for Vision-Language Instruction Following Inspired by Real-World Use
Hebrew University（希伯来大学）；Google Research（谷歌研究院）；UCLA（加州大学洛杉矶分校）；Allen Institute for AI（艾伦人工智能研究所）；University of Washington（华盛顿大学）；UCSB（加州大学圣塔芭芭拉分校）；Stanford（斯坦福大学）；LAION
CurateGPT: A flexible language-model assisted biocuration tool
Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；Queen Mary University of London（伦敦玛丽女王大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；European Bioinformatics Institute (EMBL-EBI)（欧洲生物信息研究所（欧洲分子生物学实验室-欧洲生物信息研究所））；Berlin Institute of Health - Charité, Universitätsmedizin（柏林健康研究所-夏里特医科大学）
Instruction-Tuning Data Synthesis from Scratch via Web Reconstruction
The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州））；The Hong Kong University of Science and Technology（香港科学与技术大学）；Huawei Noah’s Ark Lab（华为诺亚实验室）
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）
EVOREFUSE: Evolutionary Prompt Optimization for Evaluation and Mitigation of LLM Over-Refusal to Pseudo-Malicious Instructions
Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University（航天信息安全部门、教育部、武汉大学计算机科学与工程学院）；Ant Group（蚂蚁集团）；Ant International（蚂蚁国际）；School of Computing Technologies, Royal Melbourne Institute of Technology（皇家墨尔本理工学院计算机技术学院）
CodeEvo: Interaction-Driven Synthesis of Code-centric Data through Hybrid and Iterative Feedback
Shanghai AI Laboratory（上海人工智能实验室）；The University of Hong Kong（香港大学）；New York University（纽约大学）；Carnegie Mellon University（卡内基梅隆大学）；Shanghai Innovation Institute（上海创新研究院）
Reference-Grounded Data Curation for Instruction-Following Thai-English Machine Translation
iApp Technology（iApp科技公司）；Intelligent Informatics and Service Innovation Research Center（智能信息学与服务创新研究中心）；Artificial Intelligence Entrepreneur Association of Thailand (AIEAT)（泰国人工智能企业家协会）；Sirindhorn International Institute of Technology, Thammasat University（诗琳通国际理工学院，法政大学）
OphIn-500K: Curating Web-Scale Visual Instructions for Scaling Ophthalmic Multimodal Large Language Models
Arizona State University（亚利桑那州立大学）；Clemson University（克莱姆森大学）；Washington University in St. Louis（圣路易斯华盛顿大学）；University of Notre Dame（诺特丹大学）；Florida State University（佛罗里达州立大学）；Rice University（里德大学）；NVIDIA（英伟达）；Mayo Clinic（梅奥诊所）
CodeBoost: Boosting Code LLMs by Squeezing Knowledge from Code Snippets with RL
Nanyang Technological University（南洋理工大学）；University of Electronic Science and Technology of China（电子科技大学）；Beihang University（北京航空航天大学）；Northeastern University, China（中国东北大学）
CV-Arena: An Open Benchmark for Instructional Computer Vision Problem Solving with Human-AI Collaborative Preferences
Texas A&M University（德克萨斯A&M大学）；Worcester Polytechnic Institute（沃斯特理工大学）；Tohoku University（东北大学）；Georgia Institute of Technology（佐治亚理工学院）；NVIDIA（英伟达）；UCSB（加州大学圣塔芭芭拉分校）；UC Merced（加州大学默塞德分校）
PrivacyMind: Large Language Models Can Be Contextual Privacy Protection Learners
University of California, Los Angeles（加州大学洛杉矶分校）；Georgia Institute of Technology（佐治亚理工学院）；Tsinghua University（清华大学）；University of California, Santa Barbara（加州大学圣巴巴拉分校）；NEC Laboratories America（美国NEC实验室）
Improving Data Efficiency via Curating LLM-Driven Rating Systems
University of California, Santa Cruz（加州大学圣克鲁兹分校）；Center for Advanced AI, Accenture（埃森哲高级人工智能中心）；BIAI, ZJUT（浙江工业大学BIAI）；D5Data.ai；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
