| 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. |
Similar Papers
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. |
Enhancing Short-Text Topic Modeling with LLM-Driven Context Expansion and Prefix-Tuned VAEs (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing topic models often lack sufficient word co-occurrence in short texts, resulting in incoherent topics. |
| Approach: | They propose to use large language models to extend short texts into more detailed sequences before applying topic modeling to solve semantic inconsistency problem. |
| Outcome: | The proposed approach significantly outperforms current state-of-the-art topic models on real-world datasets with extreme data sparsity. |
Revisiting Automated Topic Model Evaluation with Large Language Models (2023.emnlp-main)
Copied to clipboard
| Challenge: | Topic models are an unsupervised dimensionality reduction technique that help organize large text collections. |
| Approach: | They propose to use large language models to evaluate document output and determine optimal number of topics. |
| Outcome: | The proposed model performs better on coherence ratings of word sets than on intrustion detection. |
Large Language Models Offer an Alternative to the Traditional Approach of Topic Modelling (2024.lrec-main)
Copied to clipboard
| Challenge: | Topic modelling has found extensive use in automatically detecting significant topics within a corpus of documents, but there are certain drawbacks. |
| Approach: | They propose a framework that prompts large language models to generate topics from a given set of documents and establish evaluation protocols to assess the clustering efficacy of LLMs. |
| Outcome: | The proposed model generates relevant topic titles and adheres to human guidelines to refine and merge topics. |
TopicGPT: A Prompt-based Topic Modeling Framework (2024.naacl-long)
Copied to clipboard
| Challenge: | TopicGPT uses large language models to uncover latent topics in text . topic models represent topics as bags of words that require "reading the tea leaves" topic models also offer limited control over formatting and specificity of topics . |
| Approach: | TopicGPT uses large language models to uncover latent topics in text . authors propose a prompt-based framework that produces topics that align better with human categorizations . |
| Outcome: | TopicGPT produces topics that align better with human categorizations compared to competing methods. |
Prompt Compression for Large Language Models: A Survey (2025.naacl-long)
Copied to clipboard
| Challenge: | Current methods for improving LLM efficiency focus on optimizing the model itself, while prompt-centric methods focus on lowering the complexity of input. |
| Approach: | They propose to use prompt compression to optimize the compression encoder and combine hard and soft prompt methods to improve the efficiency of LLMs. |
| Outcome: | The proposed methods are categorized into hard prompt methods and soft prompt methods. |
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. |
LLM-Guided Semantic-Aware Clustering for Topic Modeling (2025.acl-long)
Copied to clipboard
| 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. |
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. |
Explicit Bayesian Inference to Uncover the Latent Themes of Large Language Models (2025.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) have impressive generative capabilities, yet their inner mechanisms remain largely opaque. |
| Approach: | They propose a variational autoencoder-based neural topic model to interpret LLMs generation process through an explicit Bayesian framework by inferring latent topic variables via variational inference. |
| Outcome: | The proposed model outperforms state-of-the-art topic models on intrinsic measures of coherence and diversity on multiple datasets and shows significant gains on classification and summarization tasks. |