From Selection to Generation: A Survey of LLM-based Active Learning
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866910979318611968 |
|---|---|
| author | Xia, Yu Mukherjee, Subhojyoti Xie, Zhouhang Wu, Junda Li, Xintong Aponte, Ryan Lyu, Hanjia Barrow, Joe Chen, Hongjie Dernoncourt, Franck Kveton, Branislav Yu, Tong Zhang, Ruiyi Gu, Jiuxiang Ahmed, Nesreen K. Wang, Yu Chen, Xiang Deilamsalehy, Hanieh Kim, Sungchul Hu, Zhengmian Zhao, Yue Lipka, Nedim Yoon, Seunghyun Huang, Ting-Hao Kenneth Wang, Zichao Mathur, Puneet Pal, Soumyabrata Mukherjee, Koyel Zhang, Zhehao Park, Namyong Nguyen, Thien Huu Luo, Jiebo Rossi, Ryan A. McAuley, Julian |
| author_facet | Xia, Yu Mukherjee, Subhojyoti Xie, Zhouhang Wu, Junda Li, Xintong Aponte, Ryan Lyu, Hanjia Barrow, Joe Chen, Hongjie Dernoncourt, Franck Kveton, Branislav Yu, Tong Zhang, Ruiyi Gu, Jiuxiang Ahmed, Nesreen K. Wang, Yu Chen, Xiang Deilamsalehy, Hanieh Kim, Sungchul Hu, Zhengmian Zhao, Yue Lipka, Nedim Yoon, Seunghyun Huang, Ting-Hao Kenneth Wang, Zichao Mathur, Puneet Pal, Soumyabrata Mukherjee, Koyel Zhang, Zhehao Park, Namyong Nguyen, Thien Huu Luo, Jiebo Rossi, Ryan A. McAuley, Julian |
| contents | Active Learning (AL) has been a powerful paradigm for improving model efficiency and performance by selecting the most informative data points for labeling and training. In recent active learning frameworks, Large Language Models (LLMs) have been employed not only for selection but also for generating entirely new data instances and providing more cost-effective annotations. Motivated by the increasing importance of high-quality data and efficient model training in the era of LLMs, we present a comprehensive survey on LLM-based Active Learning. We introduce an intuitive taxonomy that categorizes these techniques and discuss the transformative roles LLMs can play in the active learning loop. We further examine the impact of AL on LLM learning paradigms and its applications across various domains. Finally, we identify open challenges and propose future research directions. This survey aims to serve as an up-to-date resource for researchers and practitioners seeking to gain an intuitive understanding of LLM-based AL techniques and deploy them to new applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_11767 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Selection to Generation: A Survey of LLM-based Active Learning Xia, Yu Mukherjee, Subhojyoti Xie, Zhouhang Wu, Junda Li, Xintong Aponte, Ryan Lyu, Hanjia Barrow, Joe Chen, Hongjie Dernoncourt, Franck Kveton, Branislav Yu, Tong Zhang, Ruiyi Gu, Jiuxiang Ahmed, Nesreen K. Wang, Yu Chen, Xiang Deilamsalehy, Hanieh Kim, Sungchul Hu, Zhengmian Zhao, Yue Lipka, Nedim Yoon, Seunghyun Huang, Ting-Hao Kenneth Wang, Zichao Mathur, Puneet Pal, Soumyabrata Mukherjee, Koyel Zhang, Zhehao Park, Namyong Nguyen, Thien Huu Luo, Jiebo Rossi, Ryan A. McAuley, Julian Machine Learning Computation and Language Active Learning (AL) has been a powerful paradigm for improving model efficiency and performance by selecting the most informative data points for labeling and training. In recent active learning frameworks, Large Language Models (LLMs) have been employed not only for selection but also for generating entirely new data instances and providing more cost-effective annotations. Motivated by the increasing importance of high-quality data and efficient model training in the era of LLMs, we present a comprehensive survey on LLM-based Active Learning. We introduce an intuitive taxonomy that categorizes these techniques and discuss the transformative roles LLMs can play in the active learning loop. We further examine the impact of AL on LLM learning paradigms and its applications across various domains. Finally, we identify open challenges and propose future research directions. This survey aims to serve as an up-to-date resource for researchers and practitioners seeking to gain an intuitive understanding of LLM-based AL techniques and deploy them to new applications. |
| title | From Selection to Generation: A Survey of LLM-based Active Learning |
| topic | Machine Learning Computation and Language |
| url | https://arxiv.org/abs/2502.11767 |