diff --git a/src/transformers/generation/logits_process.py b/src/transformers/generation/logits_process.py
index 9f130dfa2e9c..6b1093761fb9 100644
--- a/src/transformers/generation/logits_process.py
+++ b/src/transformers/generation/logits_process.py
@@ -485,14 +485,65 @@ def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> to
 
 class EtaLogitsWarper(LogitsWarper):
     r"""
-    [`LogitsWarper`] that performs eta-sampling, i.e. calculates a dynamic cutoff `eta := min(epsilon, sqrt(epsilon,
-    e^-entropy(probabilities)))` and restricts to tokens with `prob >= eta`. Takes the largest min_tokens_to_keep
-    tokens if no tokens satisfy this constraint. See [Truncation Sampling as Language Model
-    Desmoothing](https://arxiv.org/abs/2210.15191) for more information.
+    [`LogitsWarper`] that performs eta-sampling, a technique to filter out tokens with probabilities below a dynamic
+    cutoff value, `eta`, which is calculated based on a combination of the hyperparameter `epsilon` and the entropy of
+    the token probabilities, i.e. `eta := min(epsilon, sqrt(epsilon, e^-entropy(probabilities)))`. Takes the largest
+    min_tokens_to_keep tokens if no tokens satisfy this constraint. It addresses the issue of poor quality in long
+    samples of text generated by neural language models leading to more coherent and fluent text. See [Truncation
+    Sampling as Language Model Desmoothing](https://arxiv.org/abs/2210.15191) for more information. Note: `do_sample`
+    must be set to `True` for this `LogitsWarper` to work.
+
 
     Args:
+        epsilon (`float`):
+            A float value in the range (0, 1). Hyperparameter used to calculate the dynamic cutoff value, `eta`. The
+            suggested values from the paper ranges from 3e-4 to 4e-3 depending on the size of the model.
+        filter_value (`float`, *optional*, defaults to `-float("Inf")`):
+            All values that are found to be below the dynamic cutoff value, `eta`, are set to this float value. This
+            parameter is useful when logits need to be modified for very low probability tokens that should be excluded
+            from generation entirely.
         min_tokens_to_keep (`int`, *optional*, defaults to 1):
-            Minimum number of tokens that cannot be filtered."""
+            Specifies the minimum number of tokens that must be kept for generation, regardless of their probabilities.
+            For example, if `min_tokens_to_keep` is set to 1, at least one token will always be kept for generation,
+            even if all tokens have probabilities below the cutoff `eta`.
+
+    Examples:
+    ```python
+    >>> # Import required libraries
+    >>> from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed
+
+    >>> # Set the model name
+    >>> model_name = "gpt2"
+
+    >>> # Initialize the model and tokenizer
+    >>> model = AutoModelForCausalLM.from_pretrained(model_name)
+    >>> tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+    >>> # Set the pad token to eos token
+    >>> model.config.pad_token_id = model.config.eos_token_id
+    >>> model.generation_config.pad_token_id = model.config.eos_token_id
+
+    >>> # The below sequence intentionally contains two subjects to show the difference between the two approaches
+    >>> sequence = "a quadcopter flight controller (RTFQ Flip MWC) that supports I2C sensors for adding things like a barometer, magnetometer, and GPS system. The officially supported sensor block (BMP180, HMC5883L on one board) is discontinued, as far as I know, everyone involved lived to sing another day. . . disorder and an extreme state of dysmetabolism characterized by extensive erythema and a significant reduction in uncovered"
+
+    >>> # Tokenize the sequence
+    >>> inputs = tokenizer(sequence, return_tensors="pt")
+
+    >>> set_seed(0)
+
+    >>> # We can see that the model is generating repeating text and also is not able to continue the sequence properly
+    >>> outputs = model.generate(inputs["input_ids"], max_length=128)
+    >>> print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+    a quadcopter flight controller (RTFQ Flip MWC) that supports I2C sensors for adding things like a barometer, magnetometer, and GPS system. The officially supported sensor block (BMP180, HMC5883L on one board) is discontinued, as far as I know, everyone involved lived to sing another day... disorder and an extreme state of dysmetabolism characterized by extensive erythema and a significant reduction in uncovered muscle mass. The patient was diagnosed with a severe erythema and a severe erythema-like condition. The patient was treated with a combination
+
+    >>> # The result is much better and coherent when we use the `eta_cutoff` parameter
+    >>> outputs = model.generate(
+    ...     inputs["input_ids"], max_length=128, do_sample=True, eta_cutoff=2e-2
+    ... )  # need to set do_sample=True for eta_cutoff to work
+    >>> print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+    a quadcopter flight controller (RTFQ Flip MWC) that supports I2C sensors for adding things like a barometer, magnetometer, and GPS system. The officially supported sensor block (BMP180, HMC5883L on one board) is discontinued, as far as I know, everyone involved lived to sing another day... disorder and an extreme state of dysmetabolism characterized by extensive erythema and a significant reduction in uncovered fatty acids. A significant loss of brain development. The individual also experienced high levels of a common psychiatric condition called schizophrenia, with an important and life threatening consequence.
+    ```
+    """
 
     def __init__(self, epsilon: float, filter_value: float = -float("Inf"), min_tokens_to_keep: int = 1):
         epsilon = float(epsilon)