Large Language Models Offer an Alternative to the Traditional Approach of Topic Modelling (2024.lrec-main)
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| 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. |
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| Challenge: | Topic models are an unsupervised dimensionality reduction technique that help organize large text collections. |
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Zongxia Li, Lorena Calvo-Bartolomé, Alexander Miserlis Hoyle, Paiheng Xu, Daniel Kofi Stephens, Juan Francisco Fung, Alden Dima, Jordan Lee Boyd-Graber
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| Challenge: | Large Language Models (LLMs) can be used to solve topic modeling challenges for short texts by contextually learning the meanings of words. |
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| 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. |
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TopicGPT: A Prompt-based Topic Modeling Framework (2024.naacl-long)
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| 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 . |
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| Challenge: | Experimental results show that topic modeling is competitive compared to closed-source methods. |
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| Challenge: | Topic modeling (TM) is a classic unsupervised learning task in the field of natural language processing. |
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LLM-XTM: Enhancing Cross-Lingual Topic Models with Large Language Models (2026.acl-long)
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| Challenge: | Existing cross-lingual topic models depend on sparse bilingual resources and often yield incoherent or weakly aligned topics. |
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