---
title: "From Selection to Generation — paper by Xia et al. (2025)"
description: "The authors formalize an LLM-driven AL loop and propose a taxonomy built on two core… 101 references, cited by 10 papers here."
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 Kenneth Huang", "Zichao Wang", "Puneet Mathur", "Soumyabrata Pal", "Koyel Mukherjee", "Zhehao Zhang", "Namyong Park", "Thien Huu Nguyen", "Jiebo Luo", "Ryan A. Rossi", "Julian McAuley"]
year: 2025
venue: "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2025.acl-long.708/"
doi: "10.18653/v1/2025.acl-long.708"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/xia-2025-selection-generation-survey-llm-based/"
---
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# From Selection to Generation: A Survey of LLM-based Active Learning

**Authors:** [Yu Xia](https://md.research.elamur.ai/free/en/authors/yu-xia/), [Subhojyoti Mukherjee](https://md.research.elamur.ai/free/en/authors/subhojyoti-mukherjee/), [Zhouhang Xie](https://md.research.elamur.ai/free/en/authors/zhouhang-xie/), [Junda Wu](https://md.research.elamur.ai/free/en/authors/junda-wu/), [Xintong Li](https://md.research.elamur.ai/free/en/authors/xintong-li/), [Ryan Aponte](https://md.research.elamur.ai/free/en/authors/ryan-aponte/), [Hanjia Lyu](https://md.research.elamur.ai/free/en/authors/hanjia-lyu/), [Joe Barrow](https://md.research.elamur.ai/free/en/authors/joe-barrow/), [Hongjie Chen](https://md.research.elamur.ai/free/en/authors/hongjie-chen/), [Franck Dernoncourt](https://md.research.elamur.ai/free/en/authors/franck-dernoncourt/), [Branislav Kveton](https://md.research.elamur.ai/free/en/authors/branislav-kveton/), [Tong Yu](https://md.research.elamur.ai/free/en/authors/tong-yu/), [Ruiyi Zhang](https://md.research.elamur.ai/free/en/authors/ruiyi-zhang/), Jiuxiang Gu, [Nesreen K. Ahmed](https://md.research.elamur.ai/free/en/authors/nesreen-k-ahmed/), [Yu Wang](https://md.research.elamur.ai/free/en/authors/yu-wang/), [Xiang Chen](https://md.research.elamur.ai/free/en/authors/xiang-chen/), Hanieh Deilamsalehy, [Sungchul Kim](https://md.research.elamur.ai/free/en/authors/sungchul-kim/), Zhengmian Hu, [Yue Zhao](https://md.research.elamur.ai/free/en/authors/yue-zhao/), [Nedim Lipka](https://md.research.elamur.ai/free/en/authors/nedim-lipka/), Seunghyun Yoon, [Ting-Hao Kenneth Huang](https://md.research.elamur.ai/free/en/authors/ting-hao-kenneth-huang/), [Zichao Wang](https://md.research.elamur.ai/free/en/authors/zichao-wang/), [Puneet Mathur](https://md.research.elamur.ai/free/en/authors/puneet-mathur/), Soumyabrata Pal, Koyel Mukherjee, [Zhehao Zhang](https://md.research.elamur.ai/free/en/authors/zhehao-zhang/), Namyong Park, [Thien Huu Nguyen](https://md.research.elamur.ai/free/en/authors/thien-huu-nguyen/), [Jiebo Luo](https://md.research.elamur.ai/free/en/authors/jiebo-luo/), [Ryan A. Rossi](https://md.research.elamur.ai/free/en/authors/ryan-a-rossi/) and [Julian McAuley](https://md.research.elamur.ai/free/en/authors/julian-mcauley/) · **Year:** 2025 · **Venue:** [Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2025](https://md.research.elamur.ai/free/en/venues/acl/2025/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2025.acl-long.708/) · [PDF](https://aclanthology.org/2025.acl-long.708.pdf) · [DOI 10.18653/v1/2025.acl-long.708](https://doi.org/10.18653/v1/2025.acl-long.708) · [OpenAlex](https://openalex.org/W4412945194)

**Citations:** 12 citations (OpenAlex, 2026-10-14) · [10](https://md.research.elamur.ai/free/en/papers/xia-2025-selection-generation-survey-llm-based/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## 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 University](https://md.research.elamur.ai/free/en/universities/carnegie-mellon-university/) — University, Pittsburgh, United States · 539 works · 179,533 citations
- [University of Southern California](https://md.research.elamur.ai/free/en/universities/university-of-southern-california/) — University, Los Angeles, United States · 162 works · 35,836 citations
- [University of California San Diego](https://md.research.elamur.ai/free/en/universities/university-of-california-san-diego/) — University, San Diego, United States · 73 works · 51,948 citations
- [University of Rochester](https://md.research.elamur.ai/free/en/universities/university-of-rochester/) — University, Rochester, United States · 36 works · 7,398 citations
- [University of Wisconsin–Madison](https://md.research.elamur.ai/free/en/universities/university-of-wisconsin-madison/) — University, Madison, United States · 42 works · 48,486 citations
- [Pennsylvania State University](https://md.research.elamur.ai/free/en/universities/pennsylvania-state-university/) — University, State College, United States · 56 works · 5,360 citations
- [Dartmouth College](https://md.research.elamur.ai/free/en/universities/dartmouth-college/) — University, Hanover, United States · 37 works · 42,625 citations
- [University of Oregon](https://md.research.elamur.ai/free/en/universities/university-of-oregon/) — University, Eugene, United States · 23 works · 3,272 citations
- [Adobe Systems](https://md.research.elamur.ai/free/en/companies/adobe-systems/) — Company, San Jose, United States · 35 works · 3,711 citations

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## 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.

```bibtex
@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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*Updated 1 November 2026*