diff --git a/src/transformers/generation/logits_process.py b/src/transformers/generation/logits_process.py
index b5eac6b557295b..9dffdd148741d1 100644
--- a/src/transformers/generation/logits_process.py
+++ b/src/transformers/generation/logits_process.py
@@ -272,7 +272,7 @@ class RepetitionPenaltyLogitsProcessor(LogitsProcessor):
     [`LogitsProcessor`] that prevents the repetition of previous tokens through an exponential penalty. This technique
     shares some similarities with coverage mechanisms and other aimed at reducing repetition. During the text
     generation process, the probability distribution for the next token is determined using a formula that incorporates
-    token scores based on their occurrence in the generated sequence. Tokens with higher scores are less likely to be
+    token scores based on their occurrence in the generated sequence. Tokens with higher scores are more likely to be
     selected. The formula can be seen in the original [paper](https://arxiv.org/pdf/1909.05858.pdf). According to the
     paper a penalty of around 1.2 yields a good balance between truthful generation and lack of repetition.
 
@@ -328,7 +328,7 @@ class EncoderRepetitionPenaltyLogitsProcessor(LogitsProcessor):
         hallucination_penalty (`float`):
             The parameter for hallucination penalty. 1.0 means no penalty.
         encoder_input_ids (`torch.LongTensor`):
-            The encoder_input_ids that should not be repeated within the decoder ids.
+            The encoder_input_ids that should be repeated within the decoder ids.
     """
 
     def __init__(self, penalty: float, encoder_input_ids: torch.LongTensor):