Text classification has long been a cornerstone of NLP, yet most prior work and benchmarks have been limited to closed-world settings, where all classes are assumed to be known in advance. In contrast, open-world learning has recently emerged as a critical paradigm for building more robust and realistic systems. However, existing benchmarks largely focus on out-of-distribution (OOD) detection, while overlooking broader challenges such as the discovery of novel categories. To address this gap, we introduce BOLT, a unified Benchmark and evaluation toolkit supporting Open-world Learning for Text classification. BOLT encompasses two representative tasks: Open-set Text Classification (OSTC), which requires models to classify in-distribution (ID) samples while rejecting OOD inputs, and Generalized Category Discovery (GCD), which aims to identify both known and novel categories from partially labeled corpora. We carefully curate 12 publicly available datasets spanning diverse domains and benchmark 22 methods, including 15 for OSTC and 7 for GCD, under a standardized protocol that explicitly accounts for varying labeled ratios and known class ratios. Our results reveal key challenges: most current methods tend to overfit training distributions and struggle to generalize to unseen classes. Moreover, by comparing our lightweight LLM-based variants with prior open-set baselines, we demonstrate the promise of leveraging LLMs for open-world text classification. BOLT provides standardized evaluation protocols that enable fair comparison and support future research in this emerging area. All datasets, baselines, and tools are available at https://github.com/CNIC-DSL/BOLT.

Generalized Category Discovery (GCD) aims to identify both known and novel categories from partially labeled data, reflecting more realistic open-world learning scenarios. However, most existing methods rely solely on one-hot discriminative supervision, leading to overfitting on seen classes and poor generalization to unseen ones. Recent advances introduce large language models (LLMs) to incorporate external semantics, yet they often suffer from semantic–label misalignment and weak semantic integration during training. We propose GenDis, a Generative–Discriminative Dual-View Co-Training framework that unifies discriminative classification and semantic label generation within an LLM. Discriminative pseudo-labels guide the formation of a separable generative latent space, enabling semantically meaningful supervision for novel classes. To ensure consistency between the two views, we employ Canonical Correlation Analysis (CCA)-based alignment and a curriculum-guided, dispersion-aware pseudo-labeling strategy for iterative refinement. Extensive experiments on five GCD benchmarks demonstrate that GenDis substantially outperforms prior methods, validating the effectiveness of dual-view co-training with semantically enriched supervision. The anonymized repository is available at https://anonymous.4open.science/r/GenDis.

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-the-art GCD methods. The code is available at https://github.com/Wenxi-Xu/TLSA.