[Feature] Add gpqa prompt from simple_evals, openai (#1080)

* add gpqa_openai_simple_eval

* 触发CI构建

* reorg

---------
Co-authored-by: Leymore <zfz-960727@163.com>

configs/datasets/gpqa/gpqa_gen.py

 from mmengine.config import read_base
 
 with read_base():
-    from .gpqa_gen_4baadb import gpqa_datasets
+    from .gpqa_openai_simple_evals_gen_5aeece import gpqa_datasets

configs/datasets/gpqa/gpqa_openai_simple_evals_gen_5aeece.py

+from opencompass.openicl.icl_prompt_template import PromptTemplate
+from opencompass.openicl.icl_retriever import ZeroRetriever
+from opencompass.openicl.icl_inferencer import GenInferencer
+from opencompass.datasets import GPQADataset_Simple_Eval, GPQA_Simple_Eval_postprocess, GPQAEvaluator
+
+# openai_simple_eval prompt
+align_prompt = """
+Answer the following multiple choice question. The last line of your response should be of the following format: 'ANSWER: $LETTER' (without quotes) where LETTER is one of ABCD. Think step by step before answering.
+
+{question}
+
+A) {A}
+B) {B}
+C) {C}
+D) {D}
+""".strip()
+
+gpqa_reader_cfg = dict(
+    input_columns=['question', 'A', 'B', 'C', 'D'],
+    output_column='answer')
+
+gpqa_infer_cfg = dict(
+    prompt_template=dict(
+        type=PromptTemplate,
+        template=dict(
+            round=[
+                dict(role='HUMAN', prompt=align_prompt),
+            ], )),
+    retriever=dict(type=ZeroRetriever),
+    inferencer=dict(type=GenInferencer))
+
+gpqa_eval_cfg = dict(evaluator=dict(type=GPQAEvaluator),
+                     pred_postprocessor=dict(type=GPQA_Simple_Eval_postprocess))
+
+gpqa_datasets = []
+gpqa_subsets = {
+    # 'extended': 'gpqa_extended.csv',
+    # 'main': 'gpqa_main.csv',
+    'diamond': 'gpqa_diamond.csv'
+}
+
+for split in list(gpqa_subsets.keys()):
+    gpqa_datasets.append(
+        dict(
+            abbr='GPQA_' + split,
+            type=GPQADataset_Simple_Eval,
+            path='./data/gpqa/',
+            name=gpqa_subsets[split],
+            reader_cfg=gpqa_reader_cfg,
+            infer_cfg=gpqa_infer_cfg,
+            eval_cfg=gpqa_eval_cfg)
+    )

opencompass/datasets/gpqa.py

 import csv
 import os
+import random
+import re
 
 from datasets import Dataset
 
 from opencompass.openicl import BaseEvaluator
-from opencompass.registry import LOAD_DATASET
+from opencompass.registry import LOAD_DATASET, TEXT_POSTPROCESSORS
 
 from .base import BaseDataset
 
@@ -57,3 +59,53 @@ class GPQAEvaluator(BaseEvaluator):
             details.append(detail)
         result = {'accuracy': 100 * correct / count, 'details': details}
         return result
+
+
+@LOAD_DATASET.register_module()
+class GPQADataset_Simple_Eval(BaseDataset):
+
+    @staticmethod
+    def load(path: str, name: str):
+        n_repeats = 4
+        data = []
+        with open(os.path.join(path, name), 'r', encoding='utf-8') as f:
+            reader = csv.reader(f, delimiter=',')
+            for row in reader:
+                if row[7] == 'Question':
+                    continue
+                question = row[7]
+                # 第一个是正确选项
+                options = [row[8], row[9], row[10], row[11]]
+                line = {'question': question}
+                line['answer'] = 'A'
+                line['options'] = options
+                data.append(line)
+
+        data_list = data * n_repeats
+        rng = random.Random(0)
+        data_list = [
+            data | {
+                'permutation': rng.sample(range(4), 4)
+            } for data in data_list
+        ]
+        for entry in data_list:
+            options = entry['options']
+            correct_options = [options[i] for i in entry['permutation']]
+            for i in range(4):
+                entry['ABCD'[i]] = correct_options[i]
+            correct_index = entry['permutation'].index(0)
+            correct_answer = 'ABCD'[correct_index]
+            entry['options'] = correct_options
+            entry['answer'] = correct_answer
+
+        dataset = Dataset.from_list(data_list)
+        return dataset
+
+
+@TEXT_POSTPROCESSORS.register_module()
+def GPQA_Simple_Eval_postprocess(text: str) -> str:
+    ANSWER_PATTERN = r'(?i)ANSWER\s*:\s*([A-D])'
+    match = re.search(ANSWER_PATTERN, text)
+    if match:
+        return match.group(1)
+    return None