  Topic models are widely used to analyze document collections. While they are
valuable for discovering latent topics in a corpus when analysts are unfamiliar
with the corpus, analysts also commonly start with an understanding of the
content present in a corpus. This may be through categories obtained from an
initial pass over the corpus or a desire to analyze the corpus through a
predefined set of categories derived from a high level theoretical framework
(e.g. political ideology). In these scenarios analysts desire a topic modeling
approach which incorporates their understanding of the corpus while supporting
various forms of interaction with the model. In this work, we present EdTM, as
an approach for label name supervised topic modeling. EdTM models topic
modeling as an assignment problem while leveraging LM/LLM based document-topic
affinities and using optimal transport for making globally coherent
topic-assignments. In experiments, we show the efficacy of our framework
compared to few-shot LLM classifiers, and topic models based on clustering and
LDA. Further, we show EdTM's ability to incorporate various forms of analyst
feedback and while remaining robust to noisy analyst inputs.
