DSL-Topic: Improving Topic Modeling by Distilling Soft Labelsfrom Language Models 事件

DSL-Topic: Improving Topic Modeling by Distilling Soft Labelsfrom Language Models arXiv:2602.17907v2 Announce Type: replace Abstract: Traditional neural topic models are typically optimized by reconstructing the document's Bag-of-Words (BoW) representations, overlooking contextual information and struggling with data sparsity. In this work, we introduce a novel topic model training framework by Distilling Soft Labels (DSL) from Language Models (LMs). To construct the contextually enriched recon