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.
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.
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.
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.
arXiv:2507. 23220v2 Announce Type: replace-cross Abstract: Traditional topic models are effective at uncovering latent themes in large text collections.
SentenceETM is a neural topic model that assigns a topic to each sentence by jointly using lexical and contextual evidence. It represents each topic with a vocabulary distribution via word embeddings and a Gaussian over sentence embeddings in a topic‑specific low‑dimensional subspace. The model exactly marginalizes the sentence topic, combining word and embedding evidence, and uses amortized variational inference for document proportions while re‑estimating Gaussian parameters in closed form.
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.
ClusterFusion is a hybrid text‑clustering framework that uses a large language model (LLM) as the core clustering engine, guided by lightweight embedding methods. The process involves embedding‑guided subset partitioning, LLM‑driven topic summarization, and LLM‑based topic assignment, allowing direct incorporation of domain knowledge and user preferences. Experiments on three public benchmarks and two new domain‑specific datasets show that ClusterFusion achieves state‑of‑the‑art performance on standard tasks and significant gains in specialized domains, with the authors releasing a new dataset and benchmark results.
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.
TopicMerge is a multi-stage pipeline that combines efficient embedding-based clustering with Large Language Model (LLM) guidance to produce high-quality topic models. The method first over-segments a corpus into fine-grained micro-clusters, then iteratively merges them using an LLM that evaluates candidate pairs based on local context. Experiments on Bills and Wiki datasets demonstrate state‑of‑the‑art clustering performance while cutting LLM calls by more than 92% compared to document‑level approaches.
Recent advancements in topic modeling have split into two main paths: efficient embedding-based methods that offer scalability but are often outperformed on standard clustering metrics, and Large Lang...
The paper introduces MARETopic, a training‑free framework that identifies topics by selecting rank‑based prototype documents from pretrained embeddings. By projecting embeddings onto a low‑dimensional manifold and building ranked neighborhood lists, a greedy algorithm picks exactly K exemplar texts whose neighborhoods cover the corpus. Two variants—MARETopic_Corr, which uses a query‑performance predictor and rank correlation, and MARETopic_Diff, which employs a rank‑based diffusion matrix—achieve higher purity and NMI on benchmark datasets and run significantly faster, while also improving topic coherence and vocabulary diversity through a novel Maximal Marginal Relevance step.
Sequence models can generalise in fundamentally distinct ways: extrapolating structures seen in training, or using their prompt to adapt to novel tasks. The prevailing strategy determines how the mode...