topic models help users understand large document collections; however, topic
models do not always find the ``right'' topics. While classical probabilistic
and anchor-based topic models have interactive variants
EdTM is a label name supervised topic modeling approach that incorporates analysts' understanding of the corpus using LM/LLM based document-topic affinities and optimal transport for making globally coherent topic assignments.