Topic modeling, a technique used to uncover hidden themes in large text datasets, has always faced a trade-off between accuracy and efficiency. Traditional methods often struggle to capture the nuances of human language, while newer, AI-powered approaches can be computationally expensive. Imagine trying to sift through thousands of documents to identify recurring themes—it's a daunting task. Now, researchers have developed a new framework called LITA (LLM-assisted Iterative Topic Augmentation) that leverages the power of large language models (LLMs) while remaining surprisingly efficient. LITA starts with user-provided seed words—hints about the topics they're interested in—and uses an embedding model to group similar documents together. Think of it like organizing your files based on keywords. But what makes LITA unique is its iterative refinement process. It pinpoints documents that are difficult to categorize and consults an LLM for guidance. This targeted use of the LLM drastically reduces the computational cost, making it much faster than other LLM-driven methods. Instead of analyzing every single document with the LLM, LITA only calls upon the AI for the tricky cases, like asking an expert for help on the most challenging questions. Experiments on two different datasets showed that LITA consistently outperforms existing methods in terms of both accuracy and efficiency. It generates more coherent and diverse topics while using significantly fewer LLM queries, making it a more practical solution for real-world applications. This breakthrough opens up exciting possibilities for analyzing large text collections, from news articles and social media posts to scientific literature and customer reviews. By combining the best of both worlds—human guidance and AI power—LITA represents a significant step forward in the field of topic modeling. While further research is needed to address limitations like the dependence on user-provided seeds, LITA offers a promising glimpse into the future of automated text analysis.