diff --git a/opencompass/configs/datasets/CARDBiomedBench/CARDBiomedBench_llmjudge_gen_99a231.py b/opencompass/configs/datasets/CARDBiomedBench/CARDBiomedBench_llmjudge_gen_99a231.py
index cbe6cf90..cf28a37c 100644
--- a/opencompass/configs/datasets/CARDBiomedBench/CARDBiomedBench_llmjudge_gen_99a231.py
+++ b/opencompass/configs/datasets/CARDBiomedBench/CARDBiomedBench_llmjudge_gen_99a231.py
@@ -1,4 +1,4 @@
-from opencompass.datasets import CARDBiomedBenchDataset, CARDBiomedBench_llmjudge_postprocess
+from opencompass.datasets import CARDBiomedBenchDataset
 from opencompass.datasets import generic_llmjudge_postprocess
 from opencompass.openicl.icl_inferencer import GenInferencer
 from opencompass.openicl.icl_prompt_template import PromptTemplate
@@ -87,7 +87,7 @@ eval_cfg = dict(
             reader_cfg=reader_cfg,
         ),
         judge_cfg=dict(),
-        dict_postprocessor=dict(type=CARDBiomedBench_llmjudge_postprocess),
+        dict_postprocessor=dict(type=generic_llmjudge_postprocess),
     ),
 )
 cardbiomedbench_dataset = dict(
diff --git a/opencompass/datasets/CARDBiomedBench.py b/opencompass/datasets/CARDBiomedBench.py
index 39ac433c..3fbc116b 100644
--- a/opencompass/datasets/CARDBiomedBench.py
+++ b/opencompass/datasets/CARDBiomedBench.py
@@ -32,76 +32,3 @@ class CARDBiomedBenchDataset(BaseDataset):
         elif prompt_mode == 'few-shot':
             pass  # TODO: Implement few-shot prompt
         return dataset
-
-
-def _generic_llmjudge_postprocess(judgement: str):
-    match = re.search(r'(A|B)', judgement)
-    grade_letter = (match.group(0) if match else 'B'
-                    )  # Default to "INCORRECT" if no match
-    return grade_letter
-
-
-def CARDBiomedBench_llmjudge_postprocess(
-    output: dict,
-    output_path: str,
-    dataset: Dataset,
-) -> dict:
-    # Get the original dataset
-    original_dataset = dataset.reader.dataset['test']
-
-    judged_answers = []
-    original_responses = []
-    references = []
-    details = []
-
-    total_correct = 0
-    total_count = 0
-
-    for k, v in output.items():
-        idx = int(k)  # Convert key to integer for indexing
-        original_responses.append(v['prediction'])
-        processed_judge = _generic_llmjudge_postprocess(v['prediction'])
-
-        sample = original_dataset[idx]
-        # Record the judgment
-        if processed_judge is not None:
-            judged_answers.append(processed_judge)
-            try:
-                gold = v['gold']
-                references.append(gold)
-            except KeyError:
-                get_logger().warning(
-                    f'No gold answer for {k}, use empty string as reference!')
-                gold = ''
-                references.append('')
-
-            # Check if the answer is correct (A means correct)
-            is_correct = processed_judge == 'A'
-            total_count += 1
-
-            if is_correct:
-                total_correct += 1
-
-            # Add to details
-            details.append({
-                'id': k,
-                'question': sample['question'],
-                'prediction': sample['prediction'],
-                'origin_prompt': v['origin_prompt'],
-                'llm_judge': processed_judge,
-                'gold': gold,
-                'is_correct': is_correct,
-            })
-
-    # Calculate overall accuracy with two decimal places
-    overall_accuracy = (round(
-        (total_correct / total_count * 100), 2) if total_count > 0 else 0.00)
-
-    # Initialize results dictionary
-    results = {
-        'accuracy': overall_accuracy,
-        'total_correct': total_correct,
-        'total_count': total_count,
-        'details': details,
-    }
-    return results