@inproceedings{zou-etal-2026-active,
title = "Active Generalized Category Discovery with Diverse {LLM} Feedback",
author = "Zou, Henry Peng and
Singh, Siffi and
Nian, Yi and
He, Jianfeng and
Cai, Jason and
Mansour, Saab and
Su, Hang",
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.358/",
pages = "7637--7658",
ISBN = "979-8-89176-380-7",
abstract = "Generalized Category Discovery (GCD) is a practical and challenging open-world task that aims to recognize both known and novel categories in unlabeled data using limited labeled data from known categories. Due to the lack of supervision, previous GCD methods face significant challenges, such as difficulty in rectifying errors for confusing instances, and inability to effectively uncover and leverage the semantic meanings of discovered clusters. Therefore, additional annotations are usually required for real-world applicability. However, human annotation is extremely costly and inefficient. To address these issues, we propose DeLFGCD , a unified framework for generalized category discovery that actively learns from diverse and collaborative LLM feedback. Our approach leverages three different types of LLM feedback to: (1) improve instance-level contrastive features, (2) generate category descriptions, and (3) align uncertain instances with LLM-selected category descriptions. Extensive experiments demonstrate the superior performance of DeLFGCD over state-of-the-art models across diverse datasets, metrics, and supervision settings."
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<abstract>Generalized Category Discovery (GCD) is a practical and challenging open-world task that aims to recognize both known and novel categories in unlabeled data using limited labeled data from known categories. Due to the lack of supervision, previous GCD methods face significant challenges, such as difficulty in rectifying errors for confusing instances, and inability to effectively uncover and leverage the semantic meanings of discovered clusters. Therefore, additional annotations are usually required for real-world applicability. However, human annotation is extremely costly and inefficient. To address these issues, we propose DeLFGCD , a unified framework for generalized category discovery that actively learns from diverse and collaborative LLM feedback. Our approach leverages three different types of LLM feedback to: (1) improve instance-level contrastive features, (2) generate category descriptions, and (3) align uncertain instances with LLM-selected category descriptions. Extensive experiments demonstrate the superior performance of DeLFGCD over state-of-the-art models across diverse datasets, metrics, and supervision settings.</abstract>
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%0 Conference Proceedings
%T Active Generalized Category Discovery with Diverse LLM Feedback
%A Zou, Henry Peng
%A Singh, Siffi
%A Nian, Yi
%A He, Jianfeng
%A Cai, Jason
%A Mansour, Saab
%A Su, Hang
%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 zou-etal-2026-active
%X Generalized Category Discovery (GCD) is a practical and challenging open-world task that aims to recognize both known and novel categories in unlabeled data using limited labeled data from known categories. Due to the lack of supervision, previous GCD methods face significant challenges, such as difficulty in rectifying errors for confusing instances, and inability to effectively uncover and leverage the semantic meanings of discovered clusters. Therefore, additional annotations are usually required for real-world applicability. However, human annotation is extremely costly and inefficient. To address these issues, we propose DeLFGCD , a unified framework for generalized category discovery that actively learns from diverse and collaborative LLM feedback. Our approach leverages three different types of LLM feedback to: (1) improve instance-level contrastive features, (2) generate category descriptions, and (3) align uncertain instances with LLM-selected category descriptions. Extensive experiments demonstrate the superior performance of DeLFGCD over state-of-the-art models across diverse datasets, metrics, and supervision settings.
%U https://aclanthology.org/2026.eacl-long.358/
%P 7637-7658
Markdown (Informal)
[Active Generalized Category Discovery with Diverse LLM Feedback](https://aclanthology.org/2026.eacl-long.358/) (Zou et al., EACL 2026)
ACL
- Henry Peng Zou, Siffi Singh, Yi Nian, Jianfeng He, Jason Cai, Saab Mansour, and Hang Su. 2026. Active Generalized Category Discovery with Diverse LLM Feedback. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7637–7658, Rabat, Morocco. Association for Computational Linguistics.