Why we should not feed LDA with tfidf
Since the StackOverflow link in the question comments seems broken, here is another reply that addresses the same question: https://stackoverflow.com/a/44789327/6470915

Direct quote:


  In fact, Blei (who developed LDA), points out in the introduction of the paper of 2003 (entitled "Latent Dirichlet Allocation") that LDA addresses the shortcomings of the TF-IDF model and leaves this approach behind. LSA is compeltely algebraic and generally (but not necessarily) uses a TF-IDF matrix, while LDA is a probabilistic model that tries to estimate probability distributions for topics in documents and words in topics. The weighting of TF-IDF is not necessary for this.


That sums it up on the high level. It would be interesting to understand more technically, why the model would perform more poorly if TF-IDF is used. Actually, there is another reply in the SO link which claims that LDA can be improved with TF-IDF.