Neural topic models are widely used to uncover latent thematic structure in text corpora, but often produce topics with limited semantic coherence and weak alignment between topics and documents. Recent refinement approaches based on large language models (LLMs) have shown promising improvements, yet typically rely on repeated generative inference, resulting in high computational cost and practical issues such as sensitivity to prompting, stochastic variability, and the introduction of out-of-vocabulary content. We propose BERT-NTM, an encoder-based semantic refinement framework for neural topic modeling that avoids generative inference altogether. Instead of generating or rewriting topic words, BERT-NTM leverages a pretrained BERT encoder to provide discriminative semantic signals during a refinement phase: topic–word distributions are refined by scoring candidate words within a topic using masked language modeling, and document–topic representations are improved by aligning document embeddings with mixtures of topic embeddings via a Maximum Mean Discrepancy (MMD) objective. Both refinement components are fully parallelizable, stable, and operate strictly within the model vocabulary. Experiments on standard topic modeling benchmarks demonstrate that BERT-NTM consistently improves topic coherence and document–topic alignment while substantially reducing runtime and GPU memory usage compared to LLM-based refinement methods.

Dynamic topic models aim to reveal how themes emerge, evolve, and dissolve in time-stamped corpora, but existing approaches still face three major challenges: (i) encoders capture bag-of-words statistics but fail to align with the rich semantic priors of large pre-trained language models, (ii) temporal linkages are often modeled as rigid one-to-one chains, limiting the ability to track non-linear evolution such as topic splits or merges, and (iii) interpretability remains shallow, relying on noisy top-word lists that obscure thematic clarity. We propose L-DNTM (LLM-Augmented for Dynamic Neural Topic Model), a variational framework designed to capture more faithful temporal trajectories. Our model integrates three key components: multi-objective distillation to inject PLM-derived semantic knowledge into the encoder, entropy-regularized optimal transport to align entire topic constellations across time for smooth yet flexible evolution, and LLM-guided refinement to sharpen topic–word distributions for improved interpretability. Extensive experiments on diverse corpora show that L-DNTM yields more coherent, temporally consistent, and interpretable topic dynamics, and further enhances downstream classification and clustering tasks.

Cross-lingual topic modeling aims to discover shared semantic structures across languages, yet existing models depend on sparse bilingual resources and often yield incoherent or weakly aligned topics. Recent LLM-based refinements improve interpretability but are costly, document-level, and prone to hallucination, with prior white-box approaches requiring inaccessible token probabilities. We propose LLM-XTM, a framework that integrates LLM-guided topic refinement with self-consistency uncertainty quantification, enabling black-box, stable, and scalable enhancement of cross-lingual topic models. Experiments on multilingual corpora show that LLM-XTM achieves superior topic coherence and alignment while reducing reliance on bilingual dictionaries and expensive LLM calls.