[Feature] Contamination analysis for MMLU, Hellaswag, and ARC_c (#699)

* Contamination analysis for ARC_c, mmlu, and Hellaswag

* update `eval_contamination.py`

* update `contamination.py` summarizer

* fix `eval_contamination.py`

* add mmlu groups for contamination analysis

configs/datasets/ARC_c/ARC_c_clean_ppl.py

+from opencompass.openicl.icl_prompt_template import PromptTemplate
+from opencompass.openicl.icl_retriever import ZeroRetriever
+from opencompass.openicl.icl_inferencer import PPLInferencer
+from opencompass.openicl.icl_evaluator import AccContaminationEvaluator
+from opencompass.datasets import ARCDatasetClean as ARCDataset
+
+ARC_c_reader_cfg = dict(
+    input_columns=['question', 'textA', 'textB', 'textC', 'textD'],
+    output_column='answerKey')
+
+ARC_c_infer_cfg = dict(
+    prompt_template=dict(
+        type=PromptTemplate,
+        template={
+            "A":
+            dict(
+                round=[
+                    dict(role="HUMAN", prompt="Question: {question}\nAnswer: "),
+                    dict(role="BOT", prompt="{textA}")
+                ], ),
+            "B":
+            dict(
+                round=[
+                    dict(role="HUMAN", prompt="Question: {question}\nAnswer: "),
+                    dict(role="BOT", prompt="{textB}")
+                ], ),
+            "C":
+            dict(
+                round=[
+                    dict(role="HUMAN", prompt="Question: {question}\nAnswer: "),
+                    dict(role="BOT", prompt="{textC}")
+                ], ),
+            "D":
+            dict(
+                round=[
+                    dict(role="HUMAN", prompt="Question: {question}\nAnswer: "),
+                    dict(role="BOT", prompt="{textD}")
+                ], ),
+        }),
+    retriever=dict(type=ZeroRetriever),
+    inferencer=dict(type=PPLInferencer))
+
+ARC_c_eval_cfg = dict(evaluator=dict(type=AccContaminationEvaluator),
+                      analyze_contamination=True)
+
+ARC_c_datasets = [
+    dict(
+        type=ARCDataset,
+        abbr='ARC-c-test',
+        path='./data/ARC/ARC-c/ARC-Challenge-Test.jsonl',
+        reader_cfg=ARC_c_reader_cfg,
+        infer_cfg=ARC_c_infer_cfg,
+        eval_cfg=ARC_c_eval_cfg)
+]

configs/datasets/hellaswag/hellaswag_clean_ppl.py

+from opencompass.openicl.icl_prompt_template import PromptTemplate
+from opencompass.openicl.icl_retriever import ZeroRetriever
+from opencompass.openicl.icl_inferencer import PPLInferencer
+from opencompass.openicl.icl_evaluator import AccContaminationEvaluator
+from opencompass.datasets import hellaswagDatasetClean as hellaswagDataset
+
+hellaswag_reader_cfg = dict(
+    input_columns=['ctx', 'A', 'B', 'C', 'D'],
+    output_column='label')
+
+hellaswag_infer_cfg = dict(
+    prompt_template=dict(
+        type=PromptTemplate,
+        template={
+            i: dict(round=[
+                dict(role="HUMAN", prompt="{ctx}"),
+                dict(role="BOT", prompt=f"{{{chr(ord('A') + i)}}}"),
+            ])
+            for i in range(4)
+        }),
+    retriever=dict(type=ZeroRetriever),
+    inferencer=dict(type=PPLInferencer))
+
+hellaswag_eval_cfg = dict(evaluator=dict(type=AccContaminationEvaluator),
+                          analyze_contamination=True)
+
+hellaswag_datasets = [
+    dict(
+        abbr='hellaswag',
+        type=hellaswagDataset,
+        path='./data/hellaswag/hellaswag.jsonl',
+        reader_cfg=hellaswag_reader_cfg,
+        infer_cfg=hellaswag_infer_cfg,
+        eval_cfg=hellaswag_eval_cfg)
+]

configs/datasets/mmlu/mmlu_clean_ppl.py

+from opencompass.openicl.icl_prompt_template import PromptTemplate
+from opencompass.openicl.icl_retriever import FixKRetriever
+from opencompass.openicl.icl_inferencer import PPLInferencer
+from opencompass.openicl.icl_evaluator import AccContaminationEvaluator
+from opencompass.datasets import MMLUDatasetClean as MMLUDataset
+
+# None of the mmlu dataset in huggingface is correctly parsed, so we use our own dataset reader
+# Please download the dataset from https://people.eecs.berkeley.edu/~hendrycks/data.tar
+
+mmlu_reader_cfg = dict(
+    input_columns=["input", "A", "B", "C", "D"],
+    output_column="target",
+    train_split='dev')
+
+mmlu_all_sets = [
+    "college_biology",
+    "college_chemistry",
+    "college_computer_science",
+    "college_mathematics",
+    "college_physics",
+    "electrical_engineering",
+    "astronomy",
+    "anatomy",
+    "abstract_algebra",
+    "machine_learning",
+    "clinical_knowledge",
+    "global_facts",
+    "management",
+    "nutrition",
+    "marketing",
+    "professional_accounting",
+    "high_school_geography",
+    "international_law",
+    "moral_scenarios",
+    "computer_security",
+    "high_school_microeconomics",
+    "professional_law",
+    "medical_genetics",
+    "professional_psychology",
+    "jurisprudence",
+    "world_religions",
+    "philosophy",
+    "virology",
+    "high_school_chemistry",
+    "public_relations",
+    "high_school_macroeconomics",
+    "human_sexuality",
+    "elementary_mathematics",
+    "high_school_physics",
+    "high_school_computer_science",
+    "high_school_european_history",
+    "business_ethics",
+    "moral_disputes",
+    "high_school_statistics",
+    "miscellaneous",
+    "formal_logic",
+    "high_school_government_and_politics",
+    "prehistory",
+    "security_studies",
+    "high_school_biology",
+    "logical_fallacies",
+    "high_school_world_history",
+    "professional_medicine",
+    "high_school_mathematics",
+    "college_medicine",
+    "high_school_us_history",
+    "sociology",
+    "econometrics",
+    "high_school_psychology",
+    "human_aging",
+    "us_foreign_policy",
+    "conceptual_physics",
+]
+
+mmlu_datasets = []
+for _name in mmlu_all_sets:
+    _hint = f'The following are multiple choice questions (with answers) about  {_name.replace("_", " ")}.\n\n'
+    mmlu_infer_cfg = dict(
+        ice_template=dict(
+            type=PromptTemplate,
+            template={
+                opt:
+                f"{{input}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\nAnswer: {opt}\n"
+                for opt in ["A", "B", "C", "D"]
+            },
+        ),
+        prompt_template=dict(
+            type=PromptTemplate,
+            template={
+                opt:
+                f"{_hint}</E>{{input}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\nAnswer: {opt}"
+                for opt in ["A", "B", "C", "D"]
+            },
+            ice_token="</E>",
+        ),
+        retriever=dict(type=FixKRetriever, fix_id_list=[0, 1, 2, 3, 4]),
+        inferencer=dict(type=PPLInferencer),
+    )
+
+    mmlu_eval_cfg = dict(evaluator=dict(type=AccContaminationEvaluator),
+                         analyze_contamination=True)
+
+    mmlu_datasets.append(
+        dict(
+            abbr=f"lukaemon_mmlu_{_name}",
+            type=MMLUDataset,
+            path="./data/mmlu/",
+            name=_name,
+            reader_cfg=mmlu_reader_cfg,
+            infer_cfg=mmlu_infer_cfg,
+            eval_cfg=mmlu_eval_cfg,
+        ))
+
+del _name, _hint

configs/eval_contamination.py

@@ -2,10 +2,13 @@ from mmengine.config import read_base
 
 with read_base():
     from .datasets.ceval.ceval_clean_ppl import ceval_datasets
+    from .datasets.mmlu.mmlu_clean_ppl import mmlu_datasets
+    from .datasets.hellaswag.hellaswag_clean_ppl import hellaswag_datasets
+    from .datasets.ARC_c.ARC_c_clean_ppl import ARC_c_datasets
     from .models.yi.hf_yi_6b import models as hf_yi_6b_model
     from .models.qwen.hf_qwen_7b import models as hf_qwen_7b_model
     from .models.hf_llama.hf_llama2_7b import models as hf_llama2_7b_model
-    from .summarizers.contamination import ceval_summarizer as summarizer
+    from .summarizers.contamination import summarizer
 
 
 datasets = [*ceval_datasets]

configs/summarizers/contamination.py

@@ -59,6 +59,69 @@ ceval_category_weights = {
     'physician': {'accuracy - clean': 24, 'accuracy - input contaminated': 1, 'accuracy - input-and-label contaminated': 24, 'accuracy - not labeled': 0},
 }
 
+mmlu_category_weights = {
+    "business_ethics": {"accuracy - clean": 44, "accuracy - input contaminated": 16, "accuracy - input-and-label contaminated": 38, "accuracy - not labeled": 1},
+    "security_studies": {"accuracy - clean": 188, "accuracy - input contaminated": 9, "accuracy - input-and-label contaminated": 47, "accuracy - not labeled": 0},
+    "high_school_us_history": {"accuracy - clean": 42, "accuracy - input contaminated": 0, "accuracy - input-and-label contaminated": 0, "accuracy - not labeled": 161},
+    "moral_disputes": {"accuracy - clean": 105, "accuracy - input contaminated": 13, "accuracy - input-and-label contaminated": 168, "accuracy - not labeled": 59},
+    "philosophy": {"accuracy - clean": 81, "accuracy - input contaminated": 11, "accuracy - input-and-label contaminated": 187, "accuracy - not labeled": 31},
+    "public_relations": {"accuracy - clean": 75, "accuracy - input contaminated": 8, "accuracy - input-and-label contaminated": 26, "accuracy - not labeled": 0},
+    "high_school_microeconomics": {"accuracy - clean": 82, "accuracy - input contaminated": 9, "accuracy - input-and-label contaminated": 146, "accuracy - not labeled": 0},
+    "human_sexuality": {"accuracy - clean": 108, "accuracy - input contaminated": 3, "accuracy - input-and-label contaminated": 15, "accuracy - not labeled": 4},
+    "professional_accounting": {"accuracy - clean": 88, "accuracy - input contaminated": 40, "accuracy - input-and-label contaminated": 152, "accuracy - not labeled": 1},
+    "high_school_government_and_politics": {"accuracy - clean": 104, "accuracy - input contaminated": 6, "accuracy - input-and-label contaminated": 82, "accuracy - not labeled": 0},
+    "sociology": {"accuracy - clean": 105, "accuracy - input contaminated": 4, "accuracy - input-and-label contaminated": 91, "accuracy - not labeled": 0},
+    "conceptual_physics": {"accuracy - clean": 79, "accuracy - input contaminated": 8, "accuracy - input-and-label contaminated": 147, "accuracy - not labeled": 0},
+    "human_aging": {"accuracy - clean": 208, "accuracy - input contaminated": 1, "accuracy - input-and-label contaminated": 13, "accuracy - not labeled": 0},
+    "high_school_psychology": {"accuracy - clean": 108, "accuracy - input contaminated": 26, "accuracy - input-and-label contaminated": 162, "accuracy - not labeled": 248},
+    "jurisprudence": {"accuracy - clean": 59, "accuracy - input contaminated": 5, "accuracy - input-and-label contaminated": 43, "accuracy - not labeled": 0},
+    "moral_scenarios": {"accuracy - clean": 320, "accuracy - input contaminated": 0, "accuracy - input-and-label contaminated": 0, "accuracy - not labeled": 574},
+    "college_medicine": {"accuracy - clean": 107, "accuracy - input contaminated": 16, "accuracy - input-and-label contaminated": 44, "accuracy - not labeled": 5},
+    "high_school_world_history": {"accuracy - clean": 61, "accuracy - input contaminated": 2, "accuracy - input-and-label contaminated": 0, "accuracy - not labeled": 173},
+    "virology": {"accuracy - clean": 104, "accuracy - input contaminated": 3, "accuracy - input-and-label contaminated": 58, "accuracy - not labeled": 0},
+    "high_school_statistics": {"accuracy - clean": 96, "accuracy - input contaminated": 43, "accuracy - input-and-label contaminated": 76, "accuracy - not labeled": 0},
+    "nutrition": {"accuracy - clean": 172, "accuracy - input contaminated": 11, "accuracy - input-and-label contaminated": 98, "accuracy - not labeled": 24},
+    "abstract_algebra": {"accuracy - clean": 84, "accuracy - input contaminated": 8, "accuracy - input-and-label contaminated": 7, "accuracy - not labeled": 0},
+    "high_school_geography": {"accuracy - clean": 91, "accuracy - input contaminated": 1, "accuracy - input-and-label contaminated": 105, "accuracy - not labeled": 0},
+    "econometrics": {"accuracy - clean": 62, "accuracy - input contaminated": 13, "accuracy - input-and-label contaminated": 38, "accuracy - not labeled": 0},
+    "marketing": {"accuracy - clean": 115, "accuracy - input contaminated": 15, "accuracy - input-and-label contaminated": 101, "accuracy - not labeled": 2},
+    "high_school_chemistry": {"accuracy - clean": 108, "accuracy - input contaminated": 25, "accuracy - input-and-label contaminated": 69, "accuracy - not labeled": 0},
+    "prehistory": {"accuracy - clean": 154, "accuracy - input contaminated": 5, "accuracy - input-and-label contaminated": 107, "accuracy - not labeled": 57},
+    "college_physics": {"accuracy - clean": 25, "accuracy - input contaminated": 20, "accuracy - input-and-label contaminated": 57, "accuracy - not labeled": 0},
+    "management": {"accuracy - clean": 35, "accuracy - input contaminated": 5, "accuracy - input-and-label contaminated": 62, "accuracy - not labeled": 0},
+    "college_biology": {"accuracy - clean": 91, "accuracy - input contaminated": 12, "accuracy - input-and-label contaminated": 40, "accuracy - not labeled": 0},
+    "high_school_biology": {"accuracy - clean": 128, "accuracy - input contaminated": 17, "accuracy - input-and-label contaminated": 135, "accuracy - not labeled": 29},
+    "high_school_physics": {"accuracy - clean": 42, "accuracy - input contaminated": 28, "accuracy - input-and-label contaminated": 80, "accuracy - not labeled": 0},
+    "logical_fallacies": {"accuracy - clean": 133, "accuracy - input contaminated": 5, "accuracy - input-and-label contaminated": 24, "accuracy - not labeled": 0},
+    "medical_genetics": {"accuracy - clean": 49, "accuracy - input contaminated": 6, "accuracy - input-and-label contaminated": 43, "accuracy - not labeled": 1},
+    "machine_learning": {"accuracy - clean": 71, "accuracy - input contaminated": 8, "accuracy - input-and-label contaminated": 32, "accuracy - not labeled": 0},
+    "professional_law": {"accuracy - clean": 401, "accuracy - input contaminated": 8, "accuracy - input-and-label contaminated": 5, "accuracy - not labeled": 1119},
+    "professional_psychology": {"accuracy - clean": 265, "accuracy - input contaminated": 9, "accuracy - input-and-label contaminated": 27, "accuracy - not labeled": 310},
+    "global_facts": {"accuracy - clean": 89, "accuracy - input contaminated": 5, "accuracy - input-and-label contaminated": 5, "accuracy - not labeled": 0},
+    "us_foreign_policy": {"accuracy - clean": 71, "accuracy - input contaminated": 3, "accuracy - input-and-label contaminated": 25, "accuracy - not labeled": 0},
+    "international_law": {"accuracy - clean": 73, "accuracy - input contaminated": 1, "accuracy - input-and-label contaminated": 46, "accuracy - not labeled": 0},
+    "clinical_knowledge": {"accuracy - clean": 172, "accuracy - input contaminated": 6, "accuracy - input-and-label contaminated": 86, "accuracy - not labeled": 0},
+    "high_school_mathematics": {"accuracy - clean": 178, "accuracy - input contaminated": 59, "accuracy - input-and-label contaminated": 32, "accuracy - not labeled": 0},
+    "high_school_computer_science": {"accuracy - clean": 62, "accuracy - input contaminated": 7, "accuracy - input-and-label contaminated": 28, "accuracy - not labeled": 2},
+    "college_computer_science": {"accuracy - clean": 68, "accuracy - input contaminated": 15, "accuracy - input-and-label contaminated": 15, "accuracy - not labeled": 1},
+    "electrical_engineering": {"accuracy - clean": 75, "accuracy - input contaminated": 8, "accuracy - input-and-label contaminated": 61, "accuracy - not labeled": 0},
+    "college_mathematics": {"accuracy - clean": 61, "accuracy - input contaminated": 13, "accuracy - input-and-label contaminated": 26, "accuracy - not labeled": 0},
+    "computer_security": {"accuracy - clean": 55, "accuracy - input contaminated": 8, "accuracy - input-and-label contaminated": 36, "accuracy - not labeled": 0},
+    "high_school_macroeconomics": {"accuracy - clean": 102, "accuracy - input contaminated": 14, "accuracy - input-and-label contaminated": 173, "accuracy - not labeled": 100},
+    "astronomy": {"accuracy - clean": 112, "accuracy - input contaminated": 4, "accuracy - input-and-label contaminated": 35, "accuracy - not labeled": 0},
+    "college_chemistry": {"accuracy - clean": 46, "accuracy - input contaminated": 19, "accuracy - input-and-label contaminated": 34, "accuracy - not labeled": 0},
+    "high_school_european_history": {"accuracy - clean": 41, "accuracy - input contaminated": 0, "accuracy - input-and-label contaminated": 0, "accuracy - not labeled": 123},
+    "miscellaneous": {"accuracy - clean": 256, "accuracy - input contaminated": 9, "accuracy - input-and-label contaminated": 40, "accuracy - not labeled": 477},
+    "formal_logic": {"accuracy - clean": 92, "accuracy - input contaminated": 12, "accuracy - input-and-label contaminated": 21, "accuracy - not labeled": 0},
+    "elementary_mathematics": {"accuracy - clean": 155, "accuracy - input contaminated": 31, "accuracy - input-and-label contaminated": 103, "accuracy - not labeled": 88},
+    "world_religions": {"accuracy - clean": 130, "accuracy - input contaminated": 4, "accuracy - input-and-label contaminated": 36, "accuracy - not labeled": 0},
+    "professional_medicine": {"accuracy - clean": 191, "accuracy - input contaminated": 43, "accuracy - input-and-label contaminated": 1, "accuracy - not labeled": 36},
+    "anatomy": {"accuracy - clean": 52, "accuracy - input contaminated": 6, "accuracy - input-and-label contaminated": 76, "accuracy - not labeled": 0},
+}
+
+
+ARC_weights = {'accuracy - clean': 836, 'accuracy - input contaminated': 53, 'accuracy - input-and-label contaminated': 283, 'accuracy - not labeled': 0}
+hellaswag_weights = {'accuracy - clean': 5169, 'accuracy - input contaminated': 37, 'accuracy - input-and-label contaminated': 673, 'accuracy - not labeled': 4163}
 
 ceval_stem = ['computer_network', 'operating_system', 'computer_architecture', 'college_programming', 'college_physics', 'college_chemistry', 'advanced_mathematics', 'probability_and_statistics', 'discrete_mathematics', 'electrical_engineer', 'metrology_engineer', 'high_school_mathematics', 'high_school_physics', 'high_school_chemistry', 'high_school_biology', 'middle_school_mathematics', 'middle_school_biology', 'middle_school_physics', 'middle_school_chemistry', 'veterinary_medicine']
 ceval_social_science = ['college_economics', 'business_administration', 'marxism', 'mao_zedong_thought', 'education_science', 'teacher_qualification', 'high_school_politics', 'high_school_geography', 'middle_school_politics', 'middle_school_geography']
@@ -67,7 +130,13 @@ ceval_other = ['civil_servant', 'sports_science', 'plant_protection', 'basic_med
 ceval_hard = ['advanced_mathematics', 'discrete_mathematics', 'probability_and_statistics', 'college_chemistry', 'college_physics', 'high_school_mathematics', 'high_school_chemistry', 'high_school_physics']
 ceval_all = ceval_stem + ceval_social_science + ceval_humanities + ceval_other
 
-name_and_subsets = [
+_mmlu_humanities = ['formal_logic', 'high_school_european_history', 'high_school_us_history', 'high_school_world_history', 'international_law', 'jurisprudence', 'logical_fallacies', 'moral_disputes', 'moral_scenarios', 'philosophy', 'prehistory', 'professional_law', 'world_religions']
+_mmlu_stem = ['abstract_algebra', 'anatomy', 'astronomy', 'college_biology', 'college_chemistry', 'college_computer_science', 'college_mathematics', 'college_physics', 'computer_security', 'conceptual_physics', 'electrical_engineering', 'elementary_mathematics', 'high_school_biology', 'high_school_chemistry', 'high_school_computer_science', 'high_school_mathematics', 'high_school_physics', 'high_school_statistics', 'machine_learning']
+_mmlu_social_science = ['econometrics', 'high_school_geography', 'high_school_government_and_politics', 'high_school_macroeconomics', 'high_school_microeconomics', 'high_school_psychology', 'human_sexuality', 'professional_psychology', 'public_relations', 'security_studies', 'sociology', 'us_foreign_policy']
+_mmlu_other = ['business_ethics', 'clinical_knowledge', 'college_medicine', 'global_facts', 'human_aging', 'management', 'marketing', 'medical_genetics', 'miscellaneous', 'nutrition', 'professional_accounting', 'professional_medicine', 'virology']
+_mmlu_all = _mmlu_humanities + _mmlu_stem + _mmlu_social_science + _mmlu_other
+
+ceval_name_and_subsets = [
     ('ceval', ceval_all),
     ('ceval-stem', ceval_stem),
     ('ceval-social-science', ceval_social_science),
@@ -76,12 +145,32 @@ name_and_subsets = [
     ('ceval-hard', ceval_hard)
 ]
 
-ceval_summary_groups = []
+mmlu_name_and_subsets = [
+    ('mmlu', _mmlu_all),
+    ('mmlu-humanities', _mmlu_humanities),
+    ('mmlu-stem', _mmlu_stem),
+    ('mmlu-social-science', _mmlu_social_science),
+    ('mmlu-other', _mmlu_other)
+]
+
+summary_groups = []
 for metric_name in ['accuracy - clean', 'accuracy - input contaminated', 'accuracy - input-and-label contaminated']:
-    for dataset_abbr, subsets in name_and_subsets:
+    for dataset_abbr, subsets in ceval_name_and_subsets:
         weights = {f'ceval-{i}': ceval_category_weights[i][metric_name] for i in subsets}
         subsets = [[f'ceval-{i}', metric_name] for i in subsets]
-        ceval_summary_groups.append(
+        summary_groups.append(
+            {
+                'name': dataset_abbr,
+                'subsets': subsets,
+                'metric': metric_name,
+                'weights': weights,
+            }
+        )
+    
+    for dataset_abbr, subsets in mmlu_name_and_subsets:
+        weights = {f'lukaemon_mmlu_{i}': mmlu_category_weights[i][metric_name] for i in subsets}
+        subsets = [[f'lukaemon_mmlu_{i}', metric_name] for i in subsets]
+        summary_groups.append(
             {
                 'name': dataset_abbr,
                 'subsets': subsets,
@@ -90,68 +179,27 @@ for metric_name in ['accuracy - clean', 'accuracy - input contaminated', 'accura
             }
         )
     
-ceval_summarizer = dict(
+    summary_groups.append(
+        {
+            'name': 'hellaswag',
+            'subsets': [['hellaswag', metric_name]],
+            'metric': metric_name,
+            'weights': {'hellaswag': hellaswag_weights[metric_name]}
+        }
+    )
+
+    summary_groups.append(
+        {
+            'name': 'ARC-c-test',
+            'subsets': [['ARC-c-test', metric_name]],
+            'metric': metric_name,
+            'weights': {'ARC-c-test': ARC_weights[metric_name]}
+        }
+    )
+
+summarizer = dict(
     type=CircularSummarizer,
     metric_types=['accuracy - clean', 'accuracy - input contaminated', 'accuracy - input-and-label contaminated'],
-    dataset_abbrs = [
-        'ceval-computer_network',
-        'ceval-operating_system',
-        'ceval-computer_architecture',
-        'ceval-college_programming',
-        'ceval-college_physics',
-        'ceval-college_chemistry',
-        'ceval-advanced_mathematics',
-        'ceval-probability_and_statistics',
-        'ceval-discrete_mathematics',
-        'ceval-electrical_engineer',
-        'ceval-metrology_engineer',
-        'ceval-high_school_mathematics',
-        'ceval-high_school_physics',
-        'ceval-high_school_chemistry',
-        'ceval-high_school_biology',
-        'ceval-middle_school_mathematics',
-        'ceval-middle_school_biology',
-        'ceval-middle_school_physics',
-        'ceval-middle_school_chemistry',
-        'ceval-veterinary_medicine',
-        'ceval-college_economics',
-        'ceval-business_administration',
-        'ceval-marxism',
-        'ceval-mao_zedong_thought',
-        'ceval-education_science',
-        'ceval-teacher_qualification',
-        'ceval-high_school_politics',
-        'ceval-high_school_geography',
-        'ceval-middle_school_politics',
-        'ceval-middle_school_geography',
-        'ceval-modern_chinese_history',
-        'ceval-ideological_and_moral_cultivation',
-        'ceval-logic',
-        'ceval-law',
-        'ceval-chinese_language_and_literature',
-        'ceval-art_studies',
-        'ceval-professional_tour_guide',
-        'ceval-legal_professional',
-        'ceval-high_school_chinese',
-        'ceval-high_school_history',
-        'ceval-middle_school_history',
-        'ceval-civil_servant',
-        'ceval-sports_science',
-        'ceval-plant_protection',
-        'ceval-basic_medicine',
-        'ceval-clinical_medicine',
-        'ceval-urban_and_rural_planner',
-        'ceval-accountant',
-        'ceval-fire_engineer',
-        'ceval-environmental_impact_assessment_engineer',
-        'ceval-tax_accountant',
-        'ceval-physician',
-        'ceval-humanities',
-        'ceval-stem',
-        'ceval-social-science',
-        'ceval-other',
-        'ceval-hard',
-        'ceval',
-    ],
-    summary_groups=ceval_summary_groups,
+    dataset_abbrs = ['ceval', 'ceval-stem', 'ceval-social-science', 'ceval-humanities', 'ceval-other', 'ceval-hard', 'mmlu', 'mmlu-humanities', 'mmlu-stem', 'mmlu-social-science', 'mmlu-other', 'hellaswag', 'ARC-c-test'],
+    summary_groups=summary_groups,
 )

opencompass/datasets/arc.py

 import json
+import os.path as osp
 
 from datasets import Dataset
 
@@ -30,3 +31,54 @@ class ARCDataset(BaseDataset):
                     'textD': question['choices'][3]['text'],
                 })
             return Dataset.from_list(rows)
+
+
+class ARCDatasetClean(BaseDataset):
+
+    # load the contamination annotations of CEval from
+    # https://github.com/liyucheng09/Contamination_Detector
+    @staticmethod
+    def load_contamination_annotations(path, split='val'):
+        import requests
+
+        assert split == 'test', 'We only have test set annotation for ARC'
+        annotation_cache_path = osp.join(
+            path, f'ARC_c_{split}_contamination_annotations.json')
+        if osp.exists(annotation_cache_path):
+            with open(annotation_cache_path, 'r') as f:
+                annotations = json.load(f)
+            return annotations
+        link_of_annotations = 'https://github.com/liyucheng09/Contamination_Detector/releases/download/v0.1.1rc/ARC_annotations.json'  # noqa
+        annotations = json.loads(requests.get(link_of_annotations).text)
+        with open(annotation_cache_path, 'w') as f:
+            json.dump(annotations, f)
+        return annotations
+
+    @staticmethod
+    def load(path: str):
+        annotations = ARCDatasetClean.load_contamination_annotations(
+            osp.dirname(path), 'test')
+        with open(path, 'r', errors='ignore') as in_f:
+            rows = []
+            for line in in_f:
+                item = json.loads(line.strip())
+                id_ = item['id']
+                question = item['question']
+                if id_ in annotations:
+                    is_clean = annotations[id_][0]
+                else:
+                    is_clean = 'not labeled'
+                if len(question['choices']) != 4:
+                    continue
+                labels = [c['label'] for c in question['choices']]
+                answerKey = 'ABCD'[labels.index(item['answerKey'])]
+                rows.append({
+                    'question': question['stem'],
+                    'answerKey': answerKey,
+                    'textA': question['choices'][0]['text'],
+                    'textB': question['choices'][1]['text'],
+                    'textC': question['choices'][2]['text'],
+                    'textD': question['choices'][3]['text'],
+                    'is_clean': is_clean,
+                })
+            return Dataset.from_list(rows)

opencompass/datasets/hellaswag.py

 import json
+import os.path as osp
 
 from datasets import Dataset
 
@@ -68,3 +69,50 @@ class hellaswagDataset_V3(BaseDataset):
                 })
         dataset = Dataset.from_list(dataset)
         return dataset
+
+
+class hellaswagDatasetClean(BaseDataset):
+
+    # load the contamination annotations of CEval from
+    # https://github.com/liyucheng09/Contamination_Detector
+    @staticmethod
+    def load_contamination_annotations(path, split='val'):
+        import requests
+
+        assert split == 'val', 'We only use val set of hellaswag'
+        annotation_cache_path = osp.join(
+            path, f'hellaswag_{split}_contamination_annotations.json')
+        if osp.exists(annotation_cache_path):
+            with open(annotation_cache_path, 'r') as f:
+                annotations = json.load(f)
+            return annotations
+        link_of_annotations = 'https://github.com/liyucheng09/Contamination_Detector/releases/download/v0.1.1rc2/hellaswag_annotations_with_line_index.json'  # noqa
+        annotations = json.loads(requests.get(link_of_annotations).text)
+        with open(annotation_cache_path, 'w') as f:
+            json.dump(annotations, f)
+        return annotations
+
+    @staticmethod
+    def load(path):
+        dataset = []
+        annotations = hellaswagDatasetClean.load_contamination_annotations(
+            osp.dirname(path))
+        with open(path, 'r', encoding='utf-8') as f:
+            for rwo_index, line in enumerate(f):
+                data = json.loads(line)
+                rwo_index = f'{rwo_index}'
+                if rwo_index in annotations:
+                    is_clean = annotations[rwo_index][0]
+                else:
+                    is_clean = 'not labeled'
+                dataset.append({
+                    'ctx': data['query'].split(': ', 2)[-1],
+                    'A': data['choices'][0],
+                    'B': data['choices'][1],
+                    'C': data['choices'][2],
+                    'D': data['choices'][3],
+                    'label': data['gold'],
+                    'is_clean': is_clean,
+                })
+        dataset = Dataset.from_list(dataset)
+        return dataset

opencompass/datasets/mmlu.py

 import csv
+import json
 import os.path as osp
 
 from datasets import Dataset, DatasetDict
@@ -31,3 +32,57 @@ class MMLUDataset(BaseDataset):
                     })
             dataset[split] = Dataset.from_list(raw_data)
         return dataset
+
+
+class MMLUDatasetClean(BaseDataset):
+
+    # load the contamination annotations of CEval from
+    # https://github.com/liyucheng09/Contamination_Detector
+    @staticmethod
+    def load_contamination_annotations(path, split='val'):
+        import requests
+
+        assert split == 'test', 'We only use test set for MMLU'
+        annotation_cache_path = osp.join(
+            path, split, f'MMLU_{split}_contamination_annotations.json')
+        if osp.exists(annotation_cache_path):
+            with open(annotation_cache_path, 'r') as f:
+                annotations = json.load(f)
+            return annotations
+        link_of_annotations = 'https://github.com/liyucheng09/Contamination_Detector/releases/download/v0.1.1rc2/mmlu_annotations.json'  # noqa
+        annotations = json.loads(requests.get(link_of_annotations).text)
+        with open(annotation_cache_path, 'w') as f:
+            json.dump(annotations, f)
+        return annotations
+
+    @staticmethod
+    def load(path: str, name: str):
+        dataset = DatasetDict()
+        for split in ['dev', 'test']:
+            raw_data = []
+            filename = osp.join(path, split, f'{name}_{split}.csv')
+            if split == 'test':
+                annotations = MMLUDatasetClean.load_contamination_annotations(
+                    path, split)
+            with open(filename, encoding='utf-8') as f:
+                reader = csv.reader(f)
+                for row_index, row in enumerate(reader):
+                    assert len(row) == 6
+                    item = {
+                        'input': row[0],
+                        'A': row[1],
+                        'B': row[2],
+                        'C': row[3],
+                        'D': row[4],
+                        'target': row[5],
+                    }
+                    if split == 'test':
+                        row_id = f'{name} {row_index}'
+                        if row_id in annotations:
+                            is_clean = annotations[row_id][0]
+                        else:
+                            is_clean = 'not labeled'
+                        item['is_clean'] = is_clean
+                    raw_data.append(item)
+            dataset[split] = Dataset.from_list(raw_data)
+        return dataset