topic modeling analyzes documents to learn meaningful patterns of words. For
documents collected in sequence, dynamic topic models capture how these
patterns vary over time. We develop the dynamic embedded topic
Diffusion-Enhanced Topic Modeling using Encoder-Decoder-based LLMs (DeTiME) leverages LLMs to produce clusterable embeddings and generate highly clustered topics with relevant content, showcasing efficiency and adaptability.