@inproceedings{romberg-etal-2026-reassessing,
title = "Reassessing Active Learning Adoption in Contemporary {NLP}: A Community Survey",
author = {Romberg, Julia and
Schr{\"o}der, Christopher and
Gonsior, Julius and
Tomanek, Katrin and
Olsson, Fredrik},
editor = "Demberg, Vera and
Inui, Kentaro and
Marquez, Llu{\'i}s",
booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.eacl-long.120/",
pages = "2621--2647",
ISBN = "979-8-89176-380-7",
abstract = "Supervised learning relies on data annotation which usually is time-consuming and therefore expensive. A longstanding strategy to reduce annotation costs is active learning, an iterative process, in which a human annotates only data instances deemed informative by a model. Research in active learning has made considerable progress, especially with the rise of large language models (LLMs). However, we still know little about how these remarkable advances have translated into real-world applications, or contributed to removing key barriers to active learning adoption. To fill in this gap, we conduct an online survey in the NLP community to collect previously intangible insights on current implementation practices, common obstacles in application, and future prospects in active learning. We also reassess the perceived relevance of data annotation and active learning as fundamental assumptions. Our findings show that data annotation is expected to remain important and active learning to stay highly relevant while benefiting from LLMs. Consistent with a community survey from over 15 years ago, three key challenges yet persist{---}setup complexity, uncertain cost reduction, and tooling{---}for which we propose alleviation strategies. We publish an anonymized version of the dataset."
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<abstract>Supervised learning relies on data annotation which usually is time-consuming and therefore expensive. A longstanding strategy to reduce annotation costs is active learning, an iterative process, in which a human annotates only data instances deemed informative by a model. Research in active learning has made considerable progress, especially with the rise of large language models (LLMs). However, we still know little about how these remarkable advances have translated into real-world applications, or contributed to removing key barriers to active learning adoption. To fill in this gap, we conduct an online survey in the NLP community to collect previously intangible insights on current implementation practices, common obstacles in application, and future prospects in active learning. We also reassess the perceived relevance of data annotation and active learning as fundamental assumptions. Our findings show that data annotation is expected to remain important and active learning to stay highly relevant while benefiting from LLMs. Consistent with a community survey from over 15 years ago, three key challenges yet persist—setup complexity, uncertain cost reduction, and tooling—for which we propose alleviation strategies. We publish an anonymized version of the dataset.</abstract>
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%0 Conference Proceedings
%T Reassessing Active Learning Adoption in Contemporary NLP: A Community Survey
%A Romberg, Julia
%A Schröder, Christopher
%A Gonsior, Julius
%A Tomanek, Katrin
%A Olsson, Fredrik
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-380-7
%F romberg-etal-2026-reassessing
%X Supervised learning relies on data annotation which usually is time-consuming and therefore expensive. A longstanding strategy to reduce annotation costs is active learning, an iterative process, in which a human annotates only data instances deemed informative by a model. Research in active learning has made considerable progress, especially with the rise of large language models (LLMs). However, we still know little about how these remarkable advances have translated into real-world applications, or contributed to removing key barriers to active learning adoption. To fill in this gap, we conduct an online survey in the NLP community to collect previously intangible insights on current implementation practices, common obstacles in application, and future prospects in active learning. We also reassess the perceived relevance of data annotation and active learning as fundamental assumptions. Our findings show that data annotation is expected to remain important and active learning to stay highly relevant while benefiting from LLMs. Consistent with a community survey from over 15 years ago, three key challenges yet persist—setup complexity, uncertain cost reduction, and tooling—for which we propose alleviation strategies. We publish an anonymized version of the dataset.
%U https://aclanthology.org/2026.eacl-long.120/
%P 2621-2647
Markdown (Informal)
[Reassessing Active Learning Adoption in Contemporary NLP: A Community Survey](https://aclanthology.org/2026.eacl-long.120/) (Romberg et al., EACL 2026)
ACL
- Julia Romberg, Christopher Schröder, Julius Gonsior, Katrin Tomanek, and Fredrik Olsson. 2026. Reassessing Active Learning Adoption in Contemporary NLP: A Community Survey. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2621–2647, Rabat, Morocco. Association for Computational Linguistics.