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

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 .