diff --git a/CHANGELOG.md b/CHANGELOG.md
index 924167abb840..6d138eba1fcf 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -45,6 +45,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Added support for dropping nodes in `utils.to_dense_batch` in case `max_num_nodes` is smaller than the number of nodes  ([#6124](https://github.com/pyg-team/pytorch_geometric/pull/6124))
 - Added the RandLA-Net architecture as an example ([#5117](https://github.com/pyg-team/pytorch_geometric/pull/5117))
 ### Changed
+- Fix the default arguments of `DataParallel` class ([#6376](https://github.com/pyg-team/pytorch_geometric/pull/6376))
 - Fix `ImbalancedSampler` on sliced `InMemoryDataset` ([#6374](https://github.com/pyg-team/pytorch_geometric/pull/6374))
 - Breaking Change: Changed the interface and implementation of `GraphMultisetTransformer` ([#6343](https://github.com/pyg-team/pytorch_geometric/pull/6343))
 - Fixed the approximate PPR variant in `transforms.GDC` to not crash on graphs with isolated nodes ([#6242](https://github.com/pyg-team/pytorch_geometric/pull/6242))
diff --git a/torch_geometric/nn/data_parallel.py b/torch_geometric/nn/data_parallel.py
index 63e1289ca38b..4ff960e0a196 100644
--- a/torch_geometric/nn/data_parallel.py
+++ b/torch_geometric/nn/data_parallel.py
@@ -34,16 +34,16 @@ class DataParallel(torch.nn.DataParallel):
         output_device (int or torch.device): Device location of output.
             (default: :obj:`device_ids[0]`)
         follow_batch (list or tuple, optional): Creates assignment batch
-            vectors for each key in the list. (default: :obj:`[]`)
+            vectors for each key in the list. (default: :obj:`None`)
         exclude_keys (list or tuple, optional): Will exclude each key in the
-            list. (default: :obj:`[]`)
+            list. (default: :obj:`None`)
     """
     def __init__(self, module, device_ids=None, output_device=None,
-                 follow_batch=[], exclude_keys=[]):
+                 follow_batch=None, exclude_keys=None):
         super().__init__(module, device_ids, output_device)
         self.src_device = torch.device(f'cuda:{self.device_ids[0]}')
-        self.follow_batch = follow_batch
-        self.exclude_keys = exclude_keys
+        self.follow_batch = follow_batch or []
+        self.exclude_keys = exclude_keys or []
 
     def forward(self, data_list):
         """"""