用少量提示词+大模型迭代优化主题模型，省成本提效果
LITA: An Efficient LLM-assisted Iterative Topic Augmentation Framework
- 用户给种子词，结合嵌入聚类与大模型迭代修正模糊文档
- 在两个数据集上优于5种基线模型，主题质量与聚类性能双提升
- 适合需要低成本高精度主题建模的科研与业务场景
主题建模广泛用于揭示文本语料中的主题结构，但传统模型在领域特定应用中常面临主题不精确、不连贯的问题。引导式方法如SeededLDA和CorEx虽能提升相关性，却依赖人工且静态。大语言模型（LLMs）具备动态主题优化潜力，但调用成本高。为此，我们提出LLM辅助的迭代主题增强框架（LITA），融合用户提供的种子词、基于嵌入的聚类与迭代优化。LITA识别少量模糊文档，利用大模型重新分配至已有或新主题，有效控制API调用成本，同时提升主题质量。在两个数据集上，通过主题质量和聚类性能指标验证，LITA优于五种基线模型：LDA、SeededLDA、CorEx、BERTopic和PromptTopic。本工作为提升主题建模与文本聚类提供了一种高效可扩展的框架。
原文摘要 · Abstract (English)
Topic modeling is widely used for uncovering thematic structures within text corpora, yet traditional models often struggle with specificity and coherence in domain-focused applications. Guided approaches, such as SeededLDA and CorEx, incorporate user-provided seed words to improve relevance but remain labor-intensive and static. Large language models (LLMs) offer potential for dynamic topic refinement and discovery, yet their application often incurs high API costs. To address these challenges, we propose the LLM-assisted Iterative Topic Augmentation framework (LITA), an LLM-assisted approach that integrates user-provided seeds with embedding-based clustering and iterative refinement. LITA identifies a small number of ambiguous documents and employs an LLM to reassign them to existing or new topics, minimizing API costs while enhancing topic quality. Experiments on two datasets across topic quality and clustering performance metrics demonstrate that LITA outperforms five baseline models, including LDA, SeededLDA, CorEx, BERTopic, and PromptTopic. Our work offers an efficient and adaptable framework for advancing topic modeling and text clustering.
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