diff --git a/src/llmtuner/dsets/preprocess.py b/src/llmtuner/dsets/preprocess.py
index b67781d7..c3af364c 100644
--- a/src/llmtuner/dsets/preprocess.py
+++ b/src/llmtuner/dsets/preprocess.py
@@ -62,8 +62,10 @@ def preprocess_dataset(
         model_inputs = {"input_ids": [], "attention_mask": [], "labels": []}
 
         for query, response, history, system in construct_example(examples):
-            input_ids, labels = [], []
+            if not (isinstance(query, str) and isinstance(response, str) and query != "" and response != ""):
+                continue
 
+            input_ids, labels = [], []
             for turn_idx, (source_ids, target_ids) in enumerate(template.encode_multiturn(
                 tokenizer, query, response, history, system
             )):
@@ -106,6 +108,9 @@ def preprocess_dataset(
         model_inputs = {"input_ids": [], "attention_mask": [], "labels": []}
         input_ids, labels = [], []
         for query, response, history, system in construct_example(examples):
+            if not (isinstance(query, str) and isinstance(response, str) and query != "" and response != ""):
+                continue
+
             for turn_idx, (source_ids, target_ids) in enumerate(template.encode_multiturn(
                 tokenizer, query, response, history, system
             )):
@@ -139,6 +144,9 @@ def preprocess_dataset(
         model_inputs = {"input_ids": [], "attention_mask": [], "labels": []}
 
         for query, response, history, system in construct_example(examples):
+            if not (isinstance(query, str) and query != ""):
+                continue
+
             input_ids, labels = template.encode_oneturn(tokenizer, query, response, history, system)
 
             if template.efficient_eos:
@@ -158,7 +166,10 @@ def preprocess_dataset(
     def preprocess_pairwise_dataset(examples):
         # build input pairs with format `<bos> X`, `Y1 <eos>` and `Y2 <eos>`
         model_inputs = {"prompt_ids": [], "chosen_ids": [], "rejected_ids": []}
-        for query, response, history, system in construct_example(examples):
+        for query, response, history, system in construct_example(examples):            
+            if not (isinstance(query, str) and isinstance(response, list) and query != "" and len(response) > 1):
+                continue
+
             prompt_ids, chosen_ids = template.encode_oneturn(tokenizer, query, response[0], history, system)
             _, rejected_ids = template.encode_oneturn(tokenizer, query, response[1], history, system)
 
@@ -203,19 +214,15 @@ def preprocess_dataset(
         print("inputs:\n{}".format(tokenizer.decode(example["input_ids"], skip_special_tokens=False)))
 
     if stage == "pt":
-        dataset = dataset.filter(lambda example: example["prompt"])
         preprocess_func = preprocess_pretrain_dataset
         print_function = print_unsupervised_dataset_example
     elif stage == "sft" and not training_args.predict_with_generate:
-        dataset = dataset.filter(lambda example: example["prompt"] and example["response"])
         preprocess_func = preprocess_packed_supervised_dataset if data_args.sft_packing else preprocess_supervised_dataset
         print_function = print_supervised_dataset_example
     elif stage == "rm":
-        dataset = dataset.filter(lambda example: example["prompt"] and len(example["response"]) > 1)
         preprocess_func = preprocess_pairwise_dataset
         print_function = print_pairwise_dataset_example
     else:
-        dataset = dataset.filter(lambda example: example["prompt"])
         preprocess_func = preprocess_unsupervised_dataset
         print_function = print_unsupervised_dataset_example
 
@@ -235,9 +242,10 @@ def preprocess_dataset(
             **kwargs
         )
 
-        try:
-            print_function(next(iter(dataset)))
-        except StopIteration:
-            raise ValueError("Empty dataset!")
+        if training_args.should_log:
+            try:
+                print_function(next(iter(dataset)))
+            except StopIteration:
+                raise ValueError("Empty dataset!")
 
         return dataset