Summary of Lita: An Efficient Llm-assisted Iterative Topic Augmentation Framework, by Chia-hsuan Chang et al.
LITA: An Efficient LLM-assisted Iterative Topic Augmentation Framework
by Chia-Hsuan Chang, Jui-Tse Tsai, Yi-Hang Tsai, San-Yih Hwang
First submitted to arxiv on: 17 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a new approach to topic modeling called LITA (Large Language Model-assisted Iterative Topic Augmentation). Traditional topic models like LDA and guided approaches like SeededLDA and CorEx struggle with specificity and coherence in domain-focused applications. Large language models (LLMs) offer potential for dynamic topic refinement and discovery, but their application often incurs high API costs. The LITA framework integrates user-provided seeds with embedding-based clustering and iterative refinement to minimize API costs while enhancing topic quality. Experiments on two datasets demonstrate that LITA outperforms five baseline models, including LDA, SeededLDA, CorEx, BERTopic, and PromptTopic. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making a new way to understand patterns in big collections of text called topic modeling. Right now, computers have trouble finding the right patterns when they’re looking at specific topics or domains. The new approach uses something called Large Language Models (LLMs) to help make the patterns more accurate and relevant. This makes it easier for computers to find the important information they need. The researchers tested their new method on two big collections of text and showed that it works better than some other methods. |
Keywords
» Artificial intelligence » Clustering » Embedding » Large language model
