Papers with latent Dirichlet allocation
Topic Modeling in Embedding Spaces (2020.tacl-1)
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| Challenge: | Existing topic models fail to learn interpretable topics when working with large and heavy-tailed vocabularies. |
| Approach: | They propose an embedded topic model that integrates word embeddings with a categorical distribution that is the natural parameter between the word’s embeddment and an embeddement of its assigned topic. |
| Outcome: | The embedded topic model outperforms existing topic models in terms of topic quality and predictive performance. |
Evaluating Topic Quality with Posterior Variability (D19-1)
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| Challenge: | Probabilistic topic models such as latent Dirichlet allocation (LDA) are widely used for NLP tasks which require the extraction of latent themes. |
| Approach: | They propose to measure topic quality using the variability of posterior distributions of probabilistic topic models. |
| Outcome: | The proposed metric achieves state-of-the-art correlations with human judgments of topic quality in experiments on three corpora. |
Augmenting word2vec with latent Dirichlet allocation within a clinical application (N19-1)
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| Challenge: | Existing models that combine latent Dirichlet allocation and word embedding for distinguishing between speakers with and without Alzheimer’s disease from transcripts of picture descriptions are not suitable for clinical binary text classification tasks. |
| Approach: | They propose three models that combine latent Dirichlet allocation and word embedding for distinguishing between speakers with and without Alzheimer’s disease from transcripts of picture descriptions. |
| Outcome: | The proposed models outperform word2vec and LDA models on a clinical binary text classification task. |
An Empirical Analysis of the Writing Styles of Persona-Assigned LLMs (2024.emnlp-main)
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| Challenge: | Recent efforts to "personalize" large language models by assigning them specific personas are limited by current knowledge of how well they perform. |
| Approach: | They use a style embedding model to analyze writing styles of persona-assigned LLMs . they find significant style differences between personas using Kullback-Leibler divergence . |
| Outcome: | The proposed model shows significant differences in writing styles among personas across socio-demographic groups. |
Tell, Don’t Show: Leveraging Language Models’ Abstractive Retellings to Model Literary Themes (2025.findings-acl)
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| Challenge: | Literature challenges traditional bag-of-words approaches for topic modeling because narrative language focuses on immersive sensory details instead of abstractive description or exposition. |
| Approach: | They propose a topic modeling approach that prompts generative language models to *tell* what passages *show*, thereby translating narratives’ surface forms into higher-level concepts and themes. |
| Outcome: | The proposed model can translate narratives’ surface forms into higher-level concepts and themes than by running LDA alone or directly asking LMs to list topics. |