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

Paper · 2025 · Yu Xia, Subhojyoti Mukherjee, Zhouhang Xie et al.

Summary

Active learning (AL) traditionally lowers labeling effort by picking the most useful unlabeled examples with metrics such as uncertainty or diversity. This survey, presented at ACL 2025, examines how LLMs change that picture. LLMs can now reason about which examples to choose, create entirely new training instances outside the unlabeled pool, and act as cheap annotators, either replacing human labelers or working alongside them.

The authors formalize an LLM-driven AL loop and propose a taxonomy built on two core components: Querying (how instances are chosen or produced) and Annotation (how labels are obtained). Using this framework, they review:

  • stopping criteria that include LLM prompting costs,
  • the effects of AL on LLM training paradigms,
  • application areas,
  • open research problems.

The survey contributes a structured map rather than new experiments. Its main point is that AL is moving from pure selection toward generation, and that LLMs can also reduce fine-tuning costs, not only labeling costs. This makes it a practical starting point for anyone building data-efficient NLP pipelines.

Key points

  • Five-step loop: Initialize, Query, Annotate, Train, Stop (given as Algorithm 1). In the Initialize step, LLMs can label or generate a warm-up set, which eases the cold-start problem.
  • Taxonomy:
    • Querying covers traditional selection, LLM-based selection, LLM-based generation, and hybrid approaches.
    • Annotation covers human, LLM-based, and hybrid labeling.
  • Training paradigms covered: active in-context learning (demonstration selection), active supervised fine-tuning, active preference alignment for RLHF, and active knowledge distillation.
  • Applications reviewed: text classification, summarization, question answering, entity matching, debiasing, translation, sentiment analysis, and question generation.
  • Open challenges:
    • mixed human and LLM costs,
    • combining several LLMs,
    • agents and RAG,
    • multimodal AL,
    • multi-aspect feedback,
    • inconsistent quality of LLM annotation across domains.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Carnegie Mellon UniversityPittsburgh · United States539 works · 179,533 citations🎓University of Southern CaliforniaLos Angeles · United States162 works · 35,836 citations🎓University of California San DiegoSan Diego · United States73 works · 51,948 citations🎓University of RochesterRochester · United States36 works · 7,398 citations🎓University of Wisconsin–MadisonMadison · United States42 works · 48,486 citations🎓Pennsylvania State UniversityState College · United States56 works · 5,360 citations🎓Dartmouth CollegeHanover · United States37 works · 42,625 citations🎓University of OregonEugene · United States23 works · 3,272 citations🏢Adobe SystemsSan Jose · United States35 works · 3,711 citations

Also on the byline: Dolby (Netherlands) · Dolby (United States).

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksDecision Sciences · 209 worksManagement Science and Operations Research · 108 works

Cite

Cite as 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 Kenneth Huang, Zichao Wang, Puneet Mathur, Soumyabrata Pal, Koyel Mukherjee, Zhehao Zhang, Namyong Park, Thien Huu Nguyen, Jiebo Luo, Ryan A. Rossi and Julian McAuley. 2025. From Selection to Generation: A Survey of LLM-based Active Learning. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2025.acl-long.708

@inproceedings{xia2025selection,
  title = {{From Selection to Generation: A Survey of LLM-based Active Learning}},
  author = {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 Kenneth Huang, Zichao Wang, Puneet Mathur, Soumyabrata Pal, Koyel Mukherjee, Zhehao Zhang, Namyong Park, Thien Huu Nguyen, Jiebo Luo, Ryan A. Rossi and Julian McAuley},
  booktitle = {Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2025},
  doi = {10.18653/v1/2025.acl-long.708},
  url = {https://doi.org/10.18653/v1/2025.acl-long.708}
}

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