From Selection to Generation: A Survey of LLM-based Active Learning

• DOI: 10.48550/arxiv.2502.11767
• Publication Date: 2025-02-17
• Authors: Yu Xia; Subhojyoti Mukherjee; Zhouhang Xie; Junda Wu; Xintong Li; Ryan Aponte; Hanjia Lyu; Joe Barrow; Hongjie Chen; Franck Dernoncourt; Branislav Kveton; Tong Yu; Ruiyi Zhang; Jiuxiang Gu; Nesreen K. Ahmed; Yu Wang; Xiang Chen; Hanieh Deilamsalehy; Sungchul Kim; Zhengmian Hu; Yue Zhao; Nedim Lipka; Seunghyun Yoon; Ting-Hao Huang; Zichao Wang; Puneet Mathur; Saheb Pal; Koyel Mukherjee; Zhehao Zhang; Namyong Park; Thien Huu Nguyen; Jiebo Luo; Ryan A. Rossi; Julian McAuley
• Keywords: FOS: Computer and information sciences; Computer Science - Machine Learning; Computer Science - Computation and Language; Computation and Language (cs.CL); Machine Learning (cs.LG)

Active Learning (AL) has been a powerful paradigm for improving model efficiency and performance by selecting the most informative data points labeling training. In recent active learning frameworks, Large Language Models (LLMs) have employed not only selection but also generating entirely new instances providing more cost-effective annotations. Motivated increasing importance of high-quality efficient training in era LLMs, we present comprehensive survey on LLM-based Learning. We introduce an intuitive taxonomy that categorizes these techniques discuss transformative roles LLMs can play loop. further examine impact AL LLM paradigms its applications across various domains. Finally, identify open challenges propose future research directions. This aims to serve as up-to-date resource researchers practitioners seeking gain understanding deploy them applications.