# Towards the TopMost: A Topic Modeling System Toolkit

arXiv: https://arxiv.org/abs/2309.06908 · HTML: https://mlsanity.com/paper/2309.06908/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 71 references that are in the map.
- First posted on arXiv 13 Sep 2023.

## Builds on (most cited first, 24 of 24)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Pointer Sentinel Mixture Models](https://mlsanity.com/paper/1609.07843/) (2016)
- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [Topic Modeling in Embedding Spaces](https://mlsanity.com/paper/1907.04907/) (2019)
- [Autoencoding Variational Inference For Topic Models](https://mlsanity.com/paper/1703.01488/) (2017)
- [Fact-Checking Complex Claims with Program-Guided Reasoning](https://mlsanity.com/paper/2305.12744/) (2023)
- [Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence](https://mlsanity.com/paper/2004.03974/) (2020)
- [Is Automated Topic Model Evaluation Broken?: The Incoherence of Coherence](https://mlsanity.com/paper/2107.02173/) (2021)
- [Topic Modeling with Wasserstein Autoencoders](https://mlsanity.com/paper/1907.12374/) (2019)
- [Topic Modelling Meets Deep Neural Networks: A Survey](https://mlsanity.com/paper/2103.00498/) (2021)
- [Neural Topic Model via Optimal Transport](https://mlsanity.com/paper/2008.13537/) (2020)
- [Contrastive Learning for Neural Topic Model](https://mlsanity.com/paper/2110.12764/) (2021)
- [Neural Models for Documents with Metadata](https://mlsanity.com/paper/1705.09296/) (2017)
- [Effective Neural Topic Modeling with Embedding Clustering Regularization](https://mlsanity.com/paper/2306.04217/) (2023)
- [The Dynamic Embedded Topic Model](https://mlsanity.com/paper/1907.05545/) (2019)
- [Short Text Topic Modeling Techniques, Applications, and Performance: A Survey](https://mlsanity.com/paper/1904.07695/) (2019)
- [Mitigating Data Sparsity for Short Text Topic Modeling by Topic-Semantic Contrastive Learning](https://mlsanity.com/paper/2211.12878/) (2022)
- [InfoCTM: A Mutual Information Maximization Perspective of Cross-Lingual Topic Modeling](https://mlsanity.com/paper/2304.03544/) (2023)
- [Sawtooth Factorial Topic Embeddings Guided Gamma Belief Network](https://mlsanity.com/paper/2107.02757/) (2021)
- [HyperMiner: Topic Taxonomy Mining with Hyperbolic Embedding](https://mlsanity.com/paper/2210.10625/) (2022)
- [Which Encoding is the Best for Text Classification in Chinese, English, Japanese and Korean?](https://mlsanity.com/paper/1708.02657/) (2017)
- [Learning Multilingual Topics from Incomparable Corpus](https://mlsanity.com/paper/1806.04270/) (2018)
- [STTM: A Tool for Short Text Topic Modeling](https://mlsanity.com/paper/1808.02215/) (2018)

## Built on by (most cited first, 21 of 21)

- [AntiLeakBench: Preventing Data Contamination by Automatically Constructing Benchmarks with Updated Real-World Knowledge](https://mlsanity.com/paper/2412.13670/) (2024)
- [AKEW: Assessing Knowledge Editing in the Wild](https://mlsanity.com/paper/2402.18909/) (2024)
- [A Survey of Recent Backdoor Attacks and Defenses in Large Language Models](https://mlsanity.com/paper/2406.06852/) (2024)
- [FASTopic: Pretrained Transformer is a Fast, Adaptive, Stable, and Transferable Topic Model](https://mlsanity.com/paper/2405.17978/) (2024)
- [Unlearning Backdoor Attacks for LLMs with Weak-to-Strong Knowledge Distillation](https://mlsanity.com/paper/2410.14425/) (2024)
- [A Survey on Neural Topic Models: Methods, Applications, and Challenges](https://mlsanity.com/paper/2401.15351/) (2024)
- [Modeling Dynamic Topics in Chain-Free Fashion by Evolution-Tracking Contrastive Learning and Unassociated Word Exclusion](https://mlsanity.com/paper/2405.17957/) (2024)
- [Topic Modeling as Multi-Objective Contrastive Optimization](https://mlsanity.com/paper/2402.07577/) (2024)
- [Are LLMs Good Zero-Shot Fallacy Classifiers?](https://mlsanity.com/paper/2410.15050/) (2024)
- [NeuroMax: Enhancing Neural Topic Modeling via Maximizing Mutual Information and Group Topic Regularization](https://mlsanity.com/paper/2409.19749/) (2024)
- [Read as You See: Guiding Unimodal LLMs for Low-Resource Explainable Harmful Meme Detection](https://mlsanity.com/paper/2506.08477/) (2025)
- [GloCOM: A Short Text Neural Topic Model via Global Clustering Context](https://mlsanity.com/paper/2412.00525/) (2024)
- [Enhancing Multimodal Entity Linking with Jaccard Distance-based Conditional Contrastive Learning and Contextual Visual Augmentation](https://mlsanity.com/paper/2501.14166/) (2025)
- [Sparse Autoencoders are Topic Models](https://mlsanity.com/paper/2511.16309/) (2025)
- [E-LDA: Toward Interpretable LDA Topic Models with Strong Guarantees in Logarithmic Parallel Time](https://mlsanity.com/paper/2506.07747/) (2025)
- [Hierarchical Graph Topic Modeling with Topic Tree-based Transformer](https://mlsanity.com/paper/2502.11345/) (2025)
- [LLM as Attention-Informed NTM and Topic Modeling as long-input Generation: Interpretability and long-Context Capability](https://mlsanity.com/paper/2510.03174/) (2025)
- [Improving Topic Modeling by Distilling Soft Labels from Language Models](https://mlsanity.com/paper/2602.17907/) (2026)
- [Disentangling Similarity and Relatedness in Topic Models](https://mlsanity.com/paper/2603.10619/) (2026)
- [Domain-Agnostic Neural Topic Modeling with Contextual Token-Level Semantic Graph Representation](https://mlsanity.com/paper/2608.16269/) (2026)
- [A Manifold-Aware Topic Modeling Approach via Rank-Based Prototypes](https://mlsanity.com/paper/2609.29630/) (2026)

## Abstract (arXiv)

Topic models have a rich history with various applications and have recently been reinvigorated by neural topic modeling. However, these numerous topic models adopt totally distinct datasets, implementations, and evaluations. This impedes quick utilization and fair comparisons, and thereby hinders their research progress and applications. To tackle this challenge, we in this paper propose a Topic Modeling System Toolkit (TopMost). Compared to existing toolkits, TopMost stands out by supporting more extensive features. It covers a broader spectrum of topic modeling scenarios with their complete lifecycles, including datasets, preprocessing, models, training, and evaluations. Thanks to its highly cohesive and decoupled modular design, TopMost enables rapid utilization, fair comparisons, and flexible extensions of diverse cutting-edge topic models. Our code, tutorials, and documentation are available at https://github.com/bobxwu/topmost.