Abstract
Active learning is designed to minimize annotation efforts by prioritizing instances that most enhance learning. However, many active learning strategies struggle with a ‘cold-start’ problem, needing substantial initial data to be effective. This limitation reduces their utility in the increasingly relevant few-shot scenarios, where the instance selection has a substantial impact. To address this, we introduce ActiveLLM, a novel active learning approach that leverages Large Language Models such as GPT-4, o1, Llama 3, or Mistral Large for selecting instances. We demonstrate that ActiveLLM significantly enhances the classification performance of BERT classifiers in few-shot scenarios, outperforming traditional active learning methods as well as improving the few-shot learning methods ADAPET, PERFECT, and SetFit. Additionally, ActiveLLM can be extended to non-few-shot scenarios, allowing for iterative selections. In this way, ActiveLLM can even help other active learning strategies to overcome their cold-start problem. Our results suggest that ActiveLLM offers a promising solution for improving model performance across various learning setups.
- Anthology ID:
- 2026.tacl-1.1
- Volume:
- [Transactions of the Association for Computational Linguistics, Volume 14](https://aclanthology.org/volumes/2026.tacl-1/)
- Month:
- Year:
- 2026
- Address:
- Cambridge, MA
- Venue:
- [TACL](https://aclanthology.org/venues/tacl/)
- SIG:
- Publisher:
- MIT Press
- Note:
- Pages:
- 1–22
- Language:
- URL:
- [https://aclanthology.org/2026.tacl-1.1/](https://aclanthology.org/2026.tacl-1.1/)
- DOI:
- [10.1162/tacl.a.63](https://doi.org/10.1162/tacl.a.63)
- Cite (ACL):
- Markus Bayer, Justin Lutz, and Christian Reuter. 2026. ActiveLLM: Large Language Model-Based Active Learning for Textual Few-Shot Scenarios .Transactions of the Association for Computational Linguistics , 14:1–22.
- Cite (Informal):
- [ActiveLLM: Large Language Model-Based Active Learning for Textual Few-Shot Scenarios](https://aclanthology.org/2026.tacl-1.1/) (Bayer et al., TACL 2026)
- PDF:
- [https://aclanthology.org/2026.tacl-1.1.pdf](https://aclanthology.org/2026.tacl-1.1.pdf)