diff --git a/keras_nlp/models/gpt2/gpt2_causal_lm.py b/keras_nlp/models/gpt2/gpt2_causal_lm.py
index b772dfa5be..8fb1b68a55 100644
--- a/keras_nlp/models/gpt2/gpt2_causal_lm.py
+++ b/keras_nlp/models/gpt2/gpt2_causal_lm.py
@@ -79,7 +79,7 @@ class GPT2CausalLM(Task):
     gpt2_lm.generate("I want to say", max_length=30)
 
     gpt2_lm.compile(sampler=keras_nlp.samplers.BeamSampler(num_beams=2))
-    gpt2_lm.generate("I want to say", max_length=30, sampler=sampler)
+    gpt2_lm.generate("I want to say", max_length=30)
     ```
 
     Map raw string to languages model logit predictions.
@@ -331,15 +331,13 @@ def generate(
 
         This method generates text based on given `prompt`. Generation will
         continue until `max_length` is met, and all tokens generated after
-        `end_token` will be truncated. The sampling approach used can be
-        controlled via the sampler argument.
+        `end_token` will be truncated. The sampling strategy can be set in
+        the `compile` method.
 
         Args:
             prompt: a string, string Tensor or string RaggedTensor. The prompt
                 text for generation.
             max_length: int. The max length of generated sequence.
-            sampler: a string or `keras_nlp.samplers.Sampler` instance. The
-                sampler to be used for text generation.
         """
         if self.preprocessor is None:
             raise ValueError(