diff --git a/src/transformers/loss/loss_utils.py b/src/transformers/loss/loss_utils.py
index df269477e9ec..1d94cb53f9c1 100644
--- a/src/transformers/loss/loss_utils.py
+++ b/src/transformers/loss/loss_utils.py
@@ -30,10 +30,21 @@ def fixed_cross_entropy(
     target: torch.Tensor,
     num_items_in_batch: torch.Tensor | None = None,
     ignore_index: int = -100,
-    **kwargs,
+    weight: torch.Tensor | None = None,
+    label_smoothing: float = 0.0,
+    **_kwargs,
 ) -> torch.Tensor:
     reduction = "sum" if num_items_in_batch is not None else "mean"
-    loss = nn.functional.cross_entropy(source, target, ignore_index=ignore_index, reduction=reduction)
+
+    loss = nn.functional.cross_entropy(
+        source,
+        target,
+        ignore_index=ignore_index,
+        reduction=reduction,
+        label_smoothing=label_smoothing,
+        weight=weight,
+    )
+
     if reduction == "sum":
         # just in case users pass an int for num_items_in_batch, which could be the case for custom trainer
         if torch.is_tensor(num_items_in_batch):