How does topic modeling with LLMs differ from traditional methods like LDA?

Traditional topic modeling methods like LDA rely on word-level counts and statistics, so they miss the semantic structure of sentences, paragraphs, and entire documents. LLM-based topic modeling captures the nuance of human language, producing more coherent and interpretable topics, and can identify complex or specialized language use, though it also brings limitations such as topic instability and hallucinations.

The source explains that conventional algorithms such as latent Dirichlet allocation (LDA) operate on word-level counts and statistical patterns. Because they do not understand meaning beyond word frequencies, these methods cannot capture the semantic structure that spans sentences, paragraphs, and entire texts. Large language models (LLMs) are different because they excel at modeling the nuanced relationships between words and larger textual units. As a result, LLM-based topic modeling yields topics that are more coherent and interpretable, and it is better able to surface complex and nuanced themes, especially in domains with jargon or specialized language. The evidence also notes that this advantage comes with new risks: LLMs may produce unstable topics across runs, hallucinate topics that do not exist in the data, fail to retrieve real topics, or reflect biases in the output.