Work

TLSA: LLM-Guided Text-Label Space Alignment with Contrastive Learning for Generalized Category Discovery

Year: 2026

Type: conference-paper

Abstract: Generalized Category Discovery (GCD) aims to classify data from partially labeled datasets by jointly recognizing known categories and discovering novel ones.Despite recent advances, existing methods still suffer from weak text-label alignment, inconsistent objectives across known and novel categories, and poor discrimination of semantically similar clusters.To mitigate these issues, we propose TLSA, a unified framework that enforces contrastive alignment between text and label representations within a shared semantic space.Specifically, we first design a label-semantic aware dual-encoder equipped with a symmetric contrastive objective to achieve text-label alignment.Then, we leverage LLM-based label induction to generate explicit and semantically meaningful names for previously unseen categories, followed by a graph-based refinement strategy that disambiguates semantically overlapping clusters through forced renaming.Finally, a confidence-aware sampling strategy ensures balanced learning across both easy and hard instances.Extensive experiments on four benchmark datasets show that TLSA consistently outperforms state-of-theart GCD methods.The code is available at https://github.com/Wenxi-Xu/TLSA.

Source: Proceedings of the conference - Association for Computational Linguistics. Meeting

DOI: 10.18653/v1/2026.acl-long.869

Authors: Wenxi Xu, Chuan Qin, Xi Chen, Chuyu Fang, Yuanchun Zhou, Hengshu Zhu