diff --git a/docs/source/reference/question_answering.rst b/docs/source/reference/question_answering.rst
index b264b83823..9afed358c0 100644
--- a/docs/source/reference/question_answering.rst
+++ b/docs/source/reference/question_answering.rst
@@ -1,7 +1,7 @@
 .. customcarditem::
    :header: Extractive Question Answering
    :card_description: Learn to answer questions pertaining to some known textual context.
-   :image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/default.svg
+   :image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/extractive_question_answering.svg
    :tags: NLP,Text
 
 .. _question_answering:
diff --git a/flash/core/data/data_module.py b/flash/core/data/data_module.py
index 054efbfb6d..fdf3f22e48 100644
--- a/flash/core/data/data_module.py
+++ b/flash/core/data/data_module.py
@@ -288,6 +288,7 @@ def _train_dataloader(self) -> DataLoader:
         else:
             drop_last = len(train_ds) > self.batch_size
         pin_memory = True
+        persistent_workers = self.num_workers > 0
 
         if self.sampler is None:
             sampler = None
@@ -317,12 +318,14 @@ def _train_dataloader(self) -> DataLoader:
             pin_memory=pin_memory,
             drop_last=drop_last,
             collate_fn=collate_fn,
+            persistent_workers=persistent_workers,
         )
 
     def _val_dataloader(self) -> DataLoader:
         val_ds: Dataset = self._val_ds() if isinstance(self._val_ds, Callable) else self._val_ds
         collate_fn = self._resolve_collate_fn(val_ds, RunningStage.VALIDATING)
         pin_memory = True
+        persistent_workers = self.num_workers > 0
 
         if isinstance(getattr(self, "trainer", None), pl.Trainer):
             return self.trainer.lightning_module.process_val_dataset(
@@ -340,12 +343,14 @@ def _val_dataloader(self) -> DataLoader:
             num_workers=self.num_workers,
             pin_memory=pin_memory,
             collate_fn=collate_fn,
+            persistent_workers=persistent_workers,
         )
 
     def _test_dataloader(self) -> DataLoader:
         test_ds: Dataset = self._test_ds() if isinstance(self._test_ds, Callable) else self._test_ds
         collate_fn = self._resolve_collate_fn(test_ds, RunningStage.TESTING)
         pin_memory = True
+        persistent_workers = False
 
         if isinstance(getattr(self, "trainer", None), pl.Trainer):
             return self.trainer.lightning_module.process_test_dataset(
@@ -363,6 +368,7 @@ def _test_dataloader(self) -> DataLoader:
             num_workers=self.num_workers,
             pin_memory=pin_memory,
             collate_fn=collate_fn,
+            persistent_workers=persistent_workers,
         )
 
     def _predict_dataloader(self) -> DataLoader:
@@ -375,6 +381,7 @@ def _predict_dataloader(self) -> DataLoader:
 
         collate_fn = self._resolve_collate_fn(predict_ds, RunningStage.PREDICTING)
         pin_memory = True
+        persistent_workers = False
 
         if isinstance(getattr(self, "trainer", None), pl.Trainer):
             return self.trainer.lightning_module.process_predict_dataset(
@@ -386,7 +393,12 @@ def _predict_dataloader(self) -> DataLoader:
             )
 
         return DataLoader(
-            predict_ds, batch_size=batch_size, num_workers=self.num_workers, pin_memory=True, collate_fn=collate_fn
+            predict_ds,
+            batch_size=batch_size,
+            num_workers=self.num_workers,
+            pin_memory=True,
+            collate_fn=collate_fn,
+            persistent_workers=persistent_workers,
         )
 
     @property