diff --git a/gensim/models/ldamodel.py b/gensim/models/ldamodel.py
index 2f8fca4768..2f66f30c52 100755
--- a/gensim/models/ldamodel.py
+++ b/gensim/models/ldamodel.py
@@ -369,7 +369,7 @@ def __init__(self, corpus=None, num_topics=100, id2word=None,
             Alternatively default prior selecting strategies can be employed by supplying a string:
 
                 * 'asymmetric': Uses a fixed normalized asymmetric prior of `1.0 / topicno`.
-                * 'default': Learns an asymmetric prior from the corpus.
+                * 'auto': Learns an asymmetric prior from the corpus.
         eta : {float, np.array, str}, optional
             A-priori belief on word probability, this can be:
 
@@ -530,8 +530,8 @@ def init_dir_prior(self, prior, name):
             If `name` == 'alpha', then the prior can be:
 
                 * an 1D array of length equal to the number of expected topics,
-                * 'asymmetric': Uses a fixed normalized assymetric prior of `1.0 / topicno`.
-                * 'default': Learns an assymetric prior from the corpus.
+                * 'asymmetric': Uses a fixed normalized asymmetric prior of `1.0 / topicno`.
+                * 'auto': Learns an asymmetric prior from the corpus.
         name : {'alpha', 'eta'}
             Whether the `prior` is parameterized by the alpha vector (1 parameter per topic)
             or by the eta (1 parameter per unique term in the vocabulary).
diff --git a/gensim/models/ldamulticore.py b/gensim/models/ldamulticore.py
index d32a709f80..168a2752c0 100644
--- a/gensim/models/ldamulticore.py
+++ b/gensim/models/ldamulticore.py
@@ -128,8 +128,8 @@ def __init__(self, corpus=None, num_topics=100, id2word=None, workers=None,
             our a-priori belief for the each topics' probability.
             Alternatively default prior selecting strategies can be employed by supplying a string:
 
-                * 'asymmetric': Uses a fixed normalized assymetric prior of `1.0 / topicno`.
-                * 'default': Learns an assymetric prior from the corpus.
+                * 'asymmetric': Uses a fixed normalized asymmetric prior of `1.0 / topicno`.
+                * 'auto': Learns an asymmetric prior from the corpus.
         eta : {float, np.array, str}, optional
             A-priori belief on word probability, this can be:
 
diff --git a/gensim/sklearn_api/atmodel.py b/gensim/sklearn_api/atmodel.py
index 69397833c0..085ed9a745 100644
--- a/gensim/sklearn_api/atmodel.py
+++ b/gensim/sklearn_api/atmodel.py
@@ -82,8 +82,8 @@ def __init__(self, num_topics=100, id2word=None, author2doc=None, doc2author=Non
             our a-priori belief for the each topics' probability.
             Alternatively default prior selecting strategies can be employed by supplying a string:
 
-                * 'asymmetric': Uses a fixed normalized assymetric prior of `1.0 / topicno`.
-                * 'default': Learns an assymetric prior from the corpus.
+                * 'asymmetric': Uses a fixed normalized asymmetric prior of `1.0 / topicno`.
+                * 'auto': Learns an asymmetric prior from the corpus.
         eta : {float, np.array, str}, optional
             A-priori belief on word probability, this can be:
 
diff --git a/gensim/sklearn_api/ldamodel.py b/gensim/sklearn_api/ldamodel.py
index 3e5d65dcc6..33f2575acc 100644
--- a/gensim/sklearn_api/ldamodel.py
+++ b/gensim/sklearn_api/ldamodel.py
@@ -60,8 +60,8 @@ def __init__(self, num_topics=100, id2word=None, chunksize=2000, passes=1, updat
             our a-priori belief for the each topics' probability.
             Alternatively default prior selecting strategies can be employed by supplying a string:
 
-                * 'asymmetric': Uses a fixed normalized assymetric prior of `1.0 / topicno`.
-                * 'default': Learns an assymetric prior from the corpus.
+                * 'asymmetric': Uses a fixed normalized asymmetric prior of `1.0 / topicno`.
+                * 'auto': Learns an asymmetric prior from the corpus.
         eta : {float, np.array, str}, optional
             A-priori belief on word probability, this can be: