Code review changes

Author: Feynman Liang

docs/mllib-clustering.md

@@ -477,15 +477,15 @@ failure recovery.
 
 All of MLlib's LDA models support:
 
-* `describeTopics`: Returns the top terms and their weights for each topics
+* `describeTopics`: Returns topics as arrays of most important terms and
+term weights
 * `topicsMatrix`: Returns a `vocabSize` by `k` matrix where each column
 is a topic
 
 *Note*: LDA is still an experimental feature under active development.
 As a result, certain features are only available in one of the two
 optimizers / models generated by the optimizer. Currently, a distributed
-model can be converted into a local model (during which we assume a
-uniform `docConcentration` document-topic prior), but not vice-versa.
+model can be converted into a local model, but not vice-versa.
 
 The following discussion will describe each optimizer/model pair
 separately.