diff --git a/src/llamafactory/data/data_utils.py b/src/llamafactory/data/data_utils.py
index 76ded47e..4666aabc 100644
--- a/src/llamafactory/data/data_utils.py
+++ b/src/llamafactory/data/data_utils.py
@@ -13,16 +13,15 @@
 # limitations under the License.
 
 from enum import Enum, unique
-from typing import TYPE_CHECKING, Dict, List, Sequence, Set, Union
+from typing import TYPE_CHECKING, Dict, List, Optional, Sequence, Set, TypedDict, Union
 
-from datasets import concatenate_datasets, interleave_datasets
+from datasets import DatasetDict, concatenate_datasets, interleave_datasets
 
 from ..extras.logging import get_logger
 
 
 if TYPE_CHECKING:
     from datasets import Dataset, IterableDataset
-    from transformers import Seq2SeqTrainingArguments
 
     from ..hparams import DataArguments
 
@@ -42,24 +41,29 @@ class Role(str, Enum):
     OBSERVATION = "observation"
 
 
+class DatasetModule(TypedDict):
+    train_dataset: Optional[Union["Dataset", "IterableDataset"]]
+    eval_dataset: Optional[Union["Dataset", "IterableDataset"]]
+
+
 def merge_dataset(
-    all_datasets: List[Union["Dataset", "IterableDataset"]],
-    data_args: "DataArguments",
-    training_args: "Seq2SeqTrainingArguments",
+    all_datasets: List[Union["Dataset", "IterableDataset"]], data_args: "DataArguments", seed: int
 ) -> Union["Dataset", "IterableDataset"]:
     if len(all_datasets) == 1:
         return all_datasets[0]
     elif data_args.mix_strategy == "concat":
         if data_args.streaming:
             logger.warning("The samples between different datasets will not be mixed in streaming mode.")
+
         return concatenate_datasets(all_datasets)
     elif data_args.mix_strategy.startswith("interleave"):
         if not data_args.streaming:
             logger.warning("We recommend using `mix_strategy=concat` in non-streaming mode.")
+
         return interleave_datasets(
             datasets=all_datasets,
             probabilities=data_args.interleave_probs,
-            seed=training_args.seed,
+            seed=seed,
             stopping_strategy="first_exhausted" if data_args.mix_strategy.endswith("under") else "all_exhausted",
         )
     else:
@@ -67,22 +71,17 @@ def merge_dataset(
 
 
 def split_dataset(
-    dataset: Union["Dataset", "IterableDataset"], data_args: "DataArguments", training_args: "Seq2SeqTrainingArguments"
-) -> Dict[str, "Dataset"]:
-    if training_args.do_train:
-        if data_args.val_size > 1e-6:  # Split the dataset
-            if data_args.streaming:
-                dataset = dataset.shuffle(buffer_size=data_args.buffer_size, seed=training_args.seed)
-                val_set = dataset.take(int(data_args.val_size))
-                train_set = dataset.skip(int(data_args.val_size))
-                return {"train_dataset": train_set, "eval_dataset": val_set}
-            else:
-                val_size = int(data_args.val_size) if data_args.val_size > 1 else data_args.val_size
-                dataset = dataset.train_test_split(test_size=val_size, seed=training_args.seed)
-                return {"train_dataset": dataset["train"], "eval_dataset": dataset["test"]}
-        else:
-            if data_args.streaming:
-                dataset = dataset.shuffle(buffer_size=data_args.buffer_size, seed=training_args.seed)
-            return {"train_dataset": dataset}
-    else:  # do_eval or do_predict
-        return {"eval_dataset": dataset}
+    dataset: Union["Dataset", "IterableDataset"], data_args: "DataArguments", seed: int
+) -> "DatasetDict":
+    r"""
+    Splits the dataset and returns a dataset dict containing train set (required) and validation set (optional).
+    """
+    if data_args.streaming:
+        dataset = dataset.shuffle(buffer_size=data_args.buffer_size, seed=seed)
+        val_set = dataset.take(int(data_args.val_size))
+        train_set = dataset.skip(int(data_args.val_size))
+        return DatasetDict({"train": train_set, "validation": val_set})
+    else:
+        val_size = int(data_args.val_size) if data_args.val_size > 1 else data_args.val_size
+        dataset = dataset.train_test_split(test_size=val_size, seed=seed)
+        return DatasetDict({"train": dataset["train"], "validation": dataset["test"]})