Update logits_process.py docstrings to clarify penalty and reward cases (attempt #2)

src/transformers/generation/logits_process.py

@@ -276,9 +276,14 @@ class RepetitionPenaltyLogitsProcessor(LogitsProcessor):
     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.
 
+    This technique can also be used to reward and thus encourage repetition in a similar manner. To penalize and reduce
+    repetition, use `penalty` values above 1.0, where a higher value penalizes more strongly. To reward and encourage
+    repetition, use `penalty` values between 0.0 and 1.0, where a lower value rewards more strongly.
+
     Args:
-        repetition_penalty (`float`):
-            The parameter for repetition penalty. 1.0 means no penalty. See [this
+        penalty (`float`):
+            The parameter for repetition penalty. 1.0 means no penalty. Above 1.0 penalizes previously generated tokens.
+            Between 0.0 and 1.0 rewards previously generated tokens. See [this 
             paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
 
     Examples:
@@ -313,7 +318,7 @@ def __init__(self, penalty: float):
     def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
         score = torch.gather(scores, 1, input_ids)
 
-        # if score < 0 then repetition penalty has to be multiplied to reduce the previous token probability
+        # if score < 0 then repetition penalty has to be multiplied to reduce the token probabilities
         score = torch.where(score < 0, score * self.penalty, score / self.penalty)
 
         scores.scatter_(1, input_ids, score)
@@ -322,11 +327,18 @@ def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> to
 
 class EncoderRepetitionPenaltyLogitsProcessor(LogitsProcessor):
     r"""
-    [`LogitsProcessor`] enforcing an exponential penalty on tokens that are not in the original input.
+    [`LogitsProcessor`] that avoids hallucination by boosting the probabilities of tokens found within the original
+    input.
+
+    This technique can also be used to reward and thus encourage hallucination (or creativity) in a similar manner. To
+    penalize and reduce hallucination, use `penalty` values above 1.0, where a higher value penalizes more strongly. To
+    reward and encourage hallucination, use `penalty` values between 0.0 and 1.0, where a lower value rewards more
+    strongly.
 
     Args:
-        hallucination_penalty (`float`):
-            The parameter for hallucination penalty. 1.0 means no penalty.
+        penalty (`float`):
+            The parameter for hallucination penalty. 1.0 means no penalty. Above 1.0 penalizes hallucination. Between
+            0.0 and 1.0 rewards hallucination.
         encoder_input_ids (`torch.LongTensor`):
             The encoder_input_ids that should be repeated within the decoder ids.
     """
@@ -342,7 +354,7 @@ def __init__(self, penalty: float, encoder_input_ids: torch.LongTensor):
     def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
         score = torch.gather(scores, 1, self.encoder_input_ids)
 
-        # if score < 0 then repetition penalty has to be multiplied to reduce the previous token probability
+        # if score < 0 then hallucination penalty has to be multiplied to increase the token probabilities
         score = torch.where(score < 0, score * self.penalty, score / self.penalty)
 
         scores.scatter_(1, self.encoder_input_ids, score)