| Challenge: | supervised topic models can incorporate arbitrary document-level features to inform topic priors, but their ability to model corpora is limited by the representation and selection of these features. |
| Approach: | They propose a generative topic model that simultaneously learns document feature representations and topics. |
| Outcome: | The proposed model outperforms DMR and LDA on three datasets and human subjects judge it more representative of associated document features. |
Similar Papers
Topic Modeling with Wasserstein Autoencoders (P19-1)
Copied to clipboard
| Challenge: | Existing probabilistic topic models are based on latent Dirichlet allocations and collapsed Gibbs sampling. |
| Approach: | They propose a novel topic model that enforces Dirichlet prior on latent document-topic vectors and a kernel kernel to minimize the Maximum Mean Discrepancy (MMD) They propose to measure the diversity of the produced topics and to use the widely used coherence measure NPMI to evaluate topic quality. |
| Outcome: | The proposed model performs better than existing topic models on real datasets. |
Sparse Parallel Training of Hierarchical Dirichlet Process Topic Models (2020.emnlp-main)
Copied to clipboard
| Challenge: | To scale non-parametric extensions of probabilistic topic models, practitioners rely increasingly on parallel and distributed systems. |
| Approach: | They propose a data-parallel sampler that utilizes all available sources of sparsity found in natural language to control memory requirements and computational complexity. |
| Outcome: | The proposed sampler is able to train a hierarchical Dirichlet process topic model on a well-known corpus (PubMed) with 8m documents and 768m tokens, using a single multi-core machine in under four days. |
Topic-Guided Reinforcement Learning with LLMs for Enhancing Multi-Document Summarization (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have shown impressive results in single-document summarization, but their performance on MDS still leaves room for improvement. |
| Approach: | They propose a topic-guided reinforcement learning approach to improve content selection in MDS . explicit prompting models with topic labels enhances the informativeness, they show . |
| Outcome: | The proposed method outperforms baselines on multi-News and multi-XScience datasets. |
Large Language Models Struggle to Describe the Haystack without Human Help: A Social Science-Inspired Evaluation of Topic Models (2025.acl-long)
Copied to clipboard
Zongxia Li, Lorena Calvo-Bartolomé, Alexander Miserlis Hoyle, Paiheng Xu, Daniel Kofi Stephens, Juan Francisco Fung, Alden Dima, Jordan Lee Boyd-Graber
| Challenge: | a common use of NLP is to facilitate the understanding of large document collections. |
| Approach: | They propose to use large language models to replace probabilistic topic models in real-world applications. |
| Outcome: | The proposed model generates more human-readable topics and shows higher average win probabilities than traditional models for data exploration. |
Neural Topic Modeling with Large Language Models in the Loop (2025.acl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have demonstrated promising capabilities in topic discovery, but their direct application to topic modeling suffers from issues such as incomplete topic coverage, misalignment of topics, and inefficiency. |
| Approach: | They propose a novel LLM-in-the-loop framework that integrates Large Language Models with Neural Topic Models (NTMs) global topics and document representations are learned through the NTM, while an LLM refines these topics using an Optimal Transport (OT)-based alignment objective. |
| Outcome: | The proposed framework improves topic interpretability while preserving the efficiency of existing NTMs. |
A Neural Generative Model for Joint Learning Topics and Topic-Specific Word Embeddings (2020.tacl-1)
Copied to clipboard
| Challenge: | Experimental results show that the proposed model outperforms word-level embedding methods in word similarity evaluation and word sense disambiguation. |
| Approach: | They propose a generative model that explores local and global context for joint learning topics and topic-specific word embeddings. |
| Outcome: | The proposed model outperforms word-level embedding methods in word similarity evaluation and word sense disambiguation. |
Large Language Models for Generative Recommendation: A Survey and Visionary Discussions (2024.lrec-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) have revolutionized the field of natural language processing but are not fully able to leverage the generative power of LLM. |
| Approach: | They examine the progress, methods, and future directions of large language models . they examine what generative recommendation is, why RS should advance to generative recommendations . |
| Outcome: | The proposed approach can be simplified to generate recommendations from the entire pool of items. |
Multi-source Neural Topic Modeling in Multi-view Embedding Spaces (2021.naacl-main)
Copied to clipboard
| Challenge: | Recent work has used pre-trained word embeddings to address data sparsity in short-text or small document collections. |
| Approach: | They propose a neural topic modeling framework using multi-view embedding spaces to improve topic quality and deal with polysemy. |
| Outcome: | The proposed framework improves topic quality and deal with polysemy. |
GraphBTM: Graph Enhanced Autoencoded Variational Inference for Biterm Topic Model (D18-1)
Copied to clipboard
| Challenge: | Conventional topic models suffer different problems in different settings. |
| Approach: | They propose a novel way to model word-pairs named biterms in the whole corpus and a Graph Convolutional Networks (GCNs) with residual connections to extract transitive features from biterm. |
| Outcome: | The proposed model generates more coherent topics compared with previous methods. |
Topic Modeling for Short Texts with Large Language Models (2024.acl-srw)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) can be used to solve topic modeling challenges for short texts by contextually learning the meanings of words. |
| Approach: | They propose two approaches to using Large Language Models (LLMs) for topic modeling: parallel prompting and sequential prompting. |
| Outcome: | The proposed methods identify more coherent topics than existing ones while maintaining the diversity of the induced topics. |