LLM-Enhanced Dual-Branch Learning for Large-Scale Multi-Label Text Classification
Large-scale multi-label text classification assigns a small subset of relevant labels to each document from a vocabulary containing thousands or tens of thousands of candidate labels. Although pretrained language models have improved semantic text representations, most representation-based approaches center their prediction pipelines on a primary encoder or combine auxiliary features within a single ranker. The complementarity between heterogeneous language models therefore remains insufficiently explored. We propose DualMLC, a dual-branch framework that processes the same document through an autoregressive decoder-only language model and a bidirectional encoder. Each branch maintains its...
| Authors | Hui Ye, Jing Zhang, Xiulong Yang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 11 September 2026 |
| Last updated at source | 11 September 2026 |
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