Tethering Broken Themes: Aligning Neural Topic Models with Labels and Authors (2025.findings-naacl)
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| Challenge: | Recent studies suggest that topic models do not align well with human intentions. |
| Approach: | They propose a method to align neural topic models with both labels and authorship information. |
| Outcome: | The proposed method improves existing models in terms of topic quality and alignment. |
Similar Papers
Are Neural Topic Models Broken? (2022.findings-emnlp)
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| Challenge: | Existing evaluation paradigms are often divorced from real-world use . recent results have challenged the validity of the prevailing model evaluation paradigm . |
| Approach: | They show that neural topic models fare worse in both respects compared to an established classical method. |
| Outcome: | The proposed method outperforms the members of the ensemble in both respects. |
Authorless Topic Models: Biasing Models Away from Known Structure (C18-1)
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| Challenge: | a recent study shows that topic models that highlight differences in authors are often not accurate . authors show that subsampling words that are highly correlated with metadata can reduce topic-metadata correlation . |
| Approach: | They propose three metrics for identifying topics that are highly correlated with metadata . they find that subsampling words causes topic-metadata correlation, improve topic stability . authors propose to use topic models to infer word distributions that correspond to recognizable themes . |
| Outcome: | The proposed model can predict which words cause the phenomenon and improve topic stability and quality. |
Benchmarking Neural Topic Models: An Empirical Study (2021.findings-acl)
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| Challenge: | Neural topic modeling has been attracting much attention recently due to its ability to leverage the advantages of both neural networks and probabilistic topic models. |
| Approach: | They propose to evaluate neural topic models in three tasks using large datasets and a set of metrics to compare them. |
| Outcome: | The proposed models perform better in the first and third tasks than the traditional probabilistic models and are better in many cases. |
Topic Modeling: Contextual Token Embeddings Are All You Need (2024.findings-emnlp)
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| Challenge: | Current neural approaches to topic modeling have not been able to solve all of the problems. |
| Approach: | They propose a topic modeling approach that uses document contextual token embeddings to find topics and find topic spans within documents. |
| Outcome: | The proposed model outperforms the current state-of-the-art models on a comprehensive set of topic model evaluation metrics. |
LLM-Guided Semantic-Aware Clustering for Topic Modeling (2025.acl-long)
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| Challenge: | Experimental results show that topic modeling is competitive compared to closed-source methods. |
| Approach: | They propose a semi-supervised topic modeling method that combines LLMs with clustering to improve topic generation and distribution. |
| Outcome: | The proposed method outperforms state-of-the-art methods that utilize GPT-4 on topic alignment and exhibits competitive performance compared to Neural Topic Models on topic quality. |
Towards Modern Topic Models: A Survey of Taxonomies and Paradigm Shifts from Algorithm-Centric to LLM-Centered Topic Analysis (2026.findings-acl)
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| Challenge: | Topic modeling (TM) is a classic unsupervised learning task in the field of natural language processing. |
| Approach: | They propose a new taxonomy that emphasizes the role of LLMs and the design of end-to-end workflows. |
| Outcome: | The proposed taxonomy emphasizes the role of LLMs and the design of end-to-end workflows. |
Practical Correlated Topic Modeling and Analysis via the Rectified Anchor Word Algorithm (D19-1)
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| Challenge: | spectral topic models lack reliability in real data and lack of practical implementations. |
| Approach: | They propose to use a spectral topic inference method to infer correlations between topics in real data and a matrix-based approach to inference. |
| Outcome: | The proposed method outperforms tensor-based methods and probabilistic methods in real data and provides a complete guide to correlated topic modeling. |
Improving the TENOR of Labeling: Re-evaluating Topic Models for Content Analysis (2024.eacl-long)
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Zongxia Li, Andrew Mao, Daniel Stephens, Pranav Goel, Emily Walpole, Alden Dima, Juan Fung, Jordan Boyd-Graber
| Challenge: | Existing evaluation metrics such as coherence and coherency are inadequate for neural topic models. |
| Approach: | They conduct the first evaluation of neural, supervised and classical topic models in an interactive task-based setting. |
| Outcome: | The proposed model performs better on cluster evaluation metrics and human evaluations than classical models on real-world tasks. |
Neural Models for Documents with Metadata (P18-1)
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| Challenge: | specialized models are often used to model text corpora without metadata . specialized algorithms are not widely used in the digital humanities and political science fields . |
| Approach: | They propose a general neural framework based on topic models to enable customization of metadata. |
| Outcome: | The proposed framework achieves strong performance with a manageable tradeoff between perplexity, coherence, and sparsity. |
Topic Balancing with Additive Regularization of Topic Models (2020.acl-srw)
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| Challenge: | Existing methods for topic modelling on unbalanced data contain topics in various proportions and documents of the relatively small theme become distributed all over the larger topics instead of being grouped into one topic. |
| Approach: | They propose a new regularizer for topic models on unbalanced data collections . they make sure this regularizer increases the quality of topic models, trained on unstructured data . |
| Outcome: | The proposed method improves the quality of topic models trained on unbalanced datasets. |