arXiv AI September 1, 2026 By Haojia Zheng, Yuyin Lu, Juntian Huang, Fan Ou, Yanghui Rao, Haoran Xie, Fu Lee Wang

Label Semantic Expansion via Label Guided Neural Topic Modeling

The paper introduces Label Semantic Expansion (LSE), a method that enriches sparse label representations by adding descriptive topic words grounded in a corpus. It proposes a Label-Guided Neural Topic Model (LGNTM) that learns label-aligned topics, integrates lexical and document semantics, and maintains consistency between topic and label structures. Experiments show that LSE and LGNTM improve label-topic alignment, label expansion, topic quality, and downstream classification performance.

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arXiv AI

Jun 4

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

arXiv:2602. 17907v2 Announce Type: replace-cross 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.

By Raymond Li, Amirhossein Abaskohi, Chuyuan Li, Gabriel Murray, Giuseppe Carenini

llms efficiency

arXiv AI

Jun 10

Improving Topic Modeling by Distilling Soft Labels from Language Models

arXiv:2602. 17907v3 Announce Type: replace-cross 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.

By Raymond Li, Amirhossein Abaskohi, Chuyuan Li, Gabriel Murray, Giuseppe Carenini

llms efficiency

arXiv Computation and Language

Sep 10

Beyond Top Words: MonoTM for Topic Modeling with Interpretable Monosemantic Features

MonoTM is an interpretable topic modeling framework that separates the estimation of document–topic mixtures from the generation of topic descriptors. It uses sparse autoencoders to extract dense, interpretable features for mixture estimation, then learns topic descriptors from a distinct set of corpus‑grounded semantic features. This approach preserves global topic structure while providing more meaningful, semantic‑unit descriptors than traditional top‑word lists.

By Una Joh, Bei Yu

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arXiv Machine Learning

Jun 30

Model Directions, Not Words: Mechanistic Topic Models Using Sparse Autoencoders

arXiv:2507. 23220v2 Announce Type: replace-cross Abstract: Traditional topic models are effective at uncovering latent themes in large text collections.

By Carolina Zheng, Nicolas Beltran-Velez, Sweta Karlekar, Claudia Shi, Achille Nazaret, Asif Mallik, Amir Feder, David M. Blei

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arXiv Machine Learning

Aug 18

Domain-Agnostic Neural Topic Modeling with Contextual Token-Level Semantic Graph Representation

arXiv:2608. 16269v1 Announce Type: cross Abstract: Recent advances in neural topic models with pre-trained language models (PLMs) have achieved strong performance by leveraging general-domain pre-training, yet their topic interpretability often degrades on specialized corpora.

By Seung-Won Seo, Won Ik Cho, Yongmin Yoo

llms rag fine-tuning benchmarks safety

arXiv Computation and Language

Aug 28

TopiCLEAR: Adaptive embedding clustering for interpretable topic discovery from short texts

TopiCLEAR is a framework that clusters document or sentence embeddings using adaptive dimensionality reduction to uncover low‑dimensional geometric structures that correspond to human‑interpretable topics. The method is evaluated on four benchmark datasets, showing strong agreement with human annotations, especially for short and informal texts. A Twitter case study demonstrates that TopiCLEAR yields more interpretable topics than LDA, recovering both annotated topic structure and coherent sub‑topics.

By Aoi Fujita, Taichi Yamamoto, Yuri Nakayama, Ryota Kobayashi

llms rag benchmarks safety