diff --git a/examples/train_lora/llama3_lora_eval.yaml b/examples/train_lora/llama3_lora_eval.yaml
index 6fcfd6ef..ad7f938b 100644
--- a/examples/train_lora/llama3_lora_eval.yaml
+++ b/examples/train_lora/llama3_lora_eval.yaml
@@ -6,8 +6,7 @@ adapter_name_or_path: saves/llama3-8b/lora/sft
 finetuning_type: lora
 
 ### dataset
-task: mmlu
-split: test
+task: mmlu_test
 template: fewshot
 lang: en
 n_shot: 5
diff --git a/src/llamafactory/eval/evaluator.py b/src/llamafactory/eval/evaluator.py
index b5269906..c5661997 100644
--- a/src/llamafactory/eval/evaluator.py
+++ b/src/llamafactory/eval/evaluator.py
@@ -73,8 +73,11 @@ class Evaluator:
         return [chr(ord("A") + offset.item()) for offset in torch.argmax(choice_probs, dim=-1)]
 
     def eval(self) -> None:
+        task = self.eval_args.task.split("_")[0]
+        split = self.eval_args.task.split("_")[1]
+
         mapping = cached_file(
-            path_or_repo_id=os.path.join(self.eval_args.task_dir, self.eval_args.task),
+            path_or_repo_id=os.path.join(self.eval_args.task_dir, task),
             filename="mapping.json",
             cache_dir=self.model_args.cache_dir,
             token=self.model_args.hf_hub_token,
@@ -88,7 +91,7 @@ class Evaluator:
         results = {}
         for subject in pbar:
             dataset = load_dataset(
-                path=os.path.join(self.eval_args.task_dir, self.eval_args.task),
+                path=os.path.join(self.eval_args.task_dir, task),
                 name=subject,
                 cache_dir=self.model_args.cache_dir,
                 download_mode=self.eval_args.download_mode,
@@ -97,12 +100,12 @@ class Evaluator:
             )
             pbar.set_postfix_str(categorys[subject]["name"])
             inputs, outputs, labels = [], [], []
-            for i in trange(len(dataset[self.data_args.split]), desc="Formatting batches", position=1, leave=False):
+            for i in trange(len(dataset[split]), desc="Formatting batches", position=1, leave=False):
                 support_set = (
                     dataset["train"].shuffle().select(range(min(self.eval_args.n_shot, len(dataset["train"]))))
                 )
                 messages = self.eval_template.format_example(
-                    target_data=dataset[self.data_args.split][i],
+                    target_data=dataset[split][i],
                     support_set=support_set,
                     subject_name=categorys[subject]["name"],
                 )
diff --git a/src/llamafactory/hparams/data_args.py b/src/llamafactory/hparams/data_args.py
index f483099d..9ae15d2d 100644
--- a/src/llamafactory/hparams/data_args.py
+++ b/src/llamafactory/hparams/data_args.py
@@ -41,10 +41,6 @@ class DataArguments:
         default="data",
         metadata={"help": "Path to the folder containing the datasets."},
     )
-    split: str = field(
-        default="train",
-        metadata={"help": "Which dataset split to use for training and evaluation."},
-    )
     cutoff_len: int = field(
         default=1024,
         metadata={"help": "The cutoff length of the tokenized inputs in the dataset."},