[Dataset] Add SciknowEval Dataset (#2070)

* first

* first

* first

* first

* SciKnowEval

* fix hash

* fix dataset-index & use official llm_judge_postprocess

* fix dataset-index.yml

* use official llmjudge_postprocess

* fix lint

* fix lint

* fix lint

* fix lint

* fix lint

* merge with main

---------

Co-authored-by: Linchen Xiao <xxllcc1993@gmail.com>

dataset-index.yml

@@ -1065,6 +1065,12 @@
     paper: https://arxiv.org/pdf/2402.09391
     configpath: opencompass/configs/datasets/SmolInstruct/smolinstruct_gen.py
     configpath_llmjudge: ''
+- SciKnowEval:
+    name: SciKnowEval
+    category: Science
+    paper: https://arxiv.org/abs/2406.09098
+    configpath: opencompass/configs/datasets/SciKnowEval/SciKnowEval_gen_ebe47d.py
+    configpath_llmjudge: opencompass/configs/datasets/SciKnowEval/SciKnowEval_llmjudge_gen_ebe47d.py
 - internsandbox:
     name: InternSandbox
     category: Reasoning/Code/Agent

opencompass/configs/datasets/SciKnowEval/SciKnowEval_gen_ebe47d.py

@@ -0,0 +1,92 @@
+from opencompass.datasets import SciKnowEvalDataset, SciKnowEvalEvaluator
+from opencompass.openicl.icl_inferencer import GenInferencer
+from opencompass.openicl.icl_prompt_template import PromptTemplate
+from opencompass.openicl.icl_retriever import ZeroRetriever
+
+ZERO_SHOT_PROMPT = '{q4}'
+
+# Reader configuration
+reader_cfg = dict(
+    input_columns=[
+        'prompt',
+        'question',
+        'choices',
+        'label',
+        'answerKey',
+        'type',
+        'domain',
+        'details',
+        'answer',
+        'q4'
+    ],
+    output_column='answerKey',
+)
+
+# Inference configuration
+infer_cfg = dict(
+    prompt_template=dict(
+        type=PromptTemplate,
+        template=dict(
+            round=[
+                dict(
+                    role='HUMAN',
+                    prompt=ZERO_SHOT_PROMPT, # prompt mode: zero-shot
+                ),
+            ],
+        ),
+    ),
+    retriever=dict(type=ZeroRetriever),
+    inferencer=dict(type=GenInferencer),
+)
+
+# Evaluation configuration
+eval_cfg = dict(
+    evaluator=dict(type=SciKnowEvalEvaluator),
+    pred_role='BOT',
+)
+sciknoweval_dataset_biology = dict(
+    type=SciKnowEvalDataset,
+    abbr='sciknoweval_biology',
+    path='hicai-zju/SciKnowEval',
+    prompt_mode='zero-shot',
+    subset='biology',
+    reader_cfg=reader_cfg,
+    infer_cfg=infer_cfg,
+    eval_cfg=eval_cfg,
+)
+
+sciknoweval_dataset_chemistry = dict(
+    type=SciKnowEvalDataset,
+    abbr='sciknoweval_chemistry',
+    path='hicai-zju/SciKnowEval',
+    subset='chemistry',
+    prompt_mode='zero-shot',
+    reader_cfg=reader_cfg,
+    infer_cfg=infer_cfg,
+    eval_cfg=eval_cfg,
+)
+
+sciknoweval_dataset_material = dict(
+    type=SciKnowEvalDataset,
+    abbr='sciknoweval_material',
+    path='hicai-zju/SciKnowEval',
+    subset='material',
+    prompt_mode='zero-shot',
+    reader_cfg=reader_cfg,
+    infer_cfg=infer_cfg,
+    eval_cfg=eval_cfg,
+)
+
+sciknoweval_dataset_physics = dict(
+    type=SciKnowEvalDataset,
+    abbr='sciknoweval_physics',
+    path='hicai-zju/SciKnowEval',
+    prompt_mode='zero-shot',
+    subset='physics',
+    reader_cfg=reader_cfg,
+    infer_cfg=infer_cfg,
+    eval_cfg=eval_cfg,
+)
+
+
+sciknoweval_datasets = [sciknoweval_dataset_biology, sciknoweval_dataset_chemistry, sciknoweval_dataset_physics, sciknoweval_dataset_material]

opencompass/configs/datasets/SciKnowEval/SciKnowEval_llmjudge_gen_ebe47d.py

@@ -0,0 +1,232 @@
+from opencompass.datasets import SciKnowEvalDataset
+from opencompass.datasets import generic_llmjudge_postprocess
+from opencompass.openicl.icl_inferencer import GenInferencer
+from opencompass.openicl.icl_prompt_template import PromptTemplate
+from opencompass.openicl.icl_retriever import ZeroRetriever
+from opencompass.evaluator import GenericLLMEvaluator
+
+ZERO_SHOT_PROMPT = '{q4}'
+
+GRADER_TEMPLATE = """
+    Please as a grading expert, judge whether the final answers given by the candidates below are consistent with the standard answers, that is, whether the candidates answered correctly. 
+    
+    Here are some evaluation criteria:
+    1. Please refer to the given standard answer. You don't need to re-generate the answer to the question because the standard answer has been given. You only need to judge whether the candidate's answer is consistent with the standard answer according to the form of the question. Don't try to answer the original question. You can assume that the standard answer is definitely correct.
+    2. Because the candidate's answer may be different from the standard answer in the form of expression, before making a judgment, please understand the question and the standard answer first, and then judge whether the candidate's answer is correct, but be careful not to try to answer the original question.
+    3. Some answers may contain multiple items, such as multiple-choice questions, multiple-select questions, fill-in-the-blank questions, etc. As long as the answer is the same as the standard answer, it is enough. For multiple-select questions and multiple-blank fill-in-the-blank questions, the candidate needs to answer all the corresponding options or blanks correctly to be considered correct.
+    4. Some answers may be expressed in different ways, such as some answers may be a mathematical expression, some answers may be a textual description, as long as the meaning expressed is the same. And some formulas are expressed in different ways, but they are equivalent and correct.
+
+    Please judge whether the following answers are consistent with the standard answer based on the above criteria. Grade the predicted answer of this new question as one of:
+    A: CORRECT 
+    B: INCORRECT
+    Just return the letters "A" or "B", with no text around it.
+
+    Here is your task. Simply reply with either CORRECT, INCORRECT. Don't apologize or correct yourself if there was a mistake; we are just trying to grade the answer.
+
+    <Original Question Begin>: Q: {q4}\n<Original Question End>\n\n
+    <Gold Target Begin>: \n{answerKey}\n<Gold Target End>\n\n
+    <Predicted Answer Begin>: \n{prediction}\n<Predicted End>\n\n
+    Judging the correctness of candidates' answers:
+""".strip()
+
+
+# Reader configuration
+reader_cfg = dict(
+    input_columns=[
+        'prompt',
+        'question',
+        'choices',
+        'label',
+        'answerKey',
+        'type',
+        'domain',
+        'details',
+        'answer',
+        'q4'
+    ],
+    output_column='answerKey',
+)
+
+# Inference configuration
+infer_cfg = dict(
+    prompt_template=dict(
+        type=PromptTemplate,
+        template=dict(
+            round=[
+                dict(
+                    
+                    role='HUMAN',
+                    prompt=ZERO_SHOT_PROMPT, # prompt mode: zero-shot
+                ),
+            ],
+        ),
+    ),
+    retriever=dict(type=ZeroRetriever),
+    inferencer=dict(type=GenInferencer),
+)
+
+# Evaluation configuration
+eval_cfg_biology = dict(
+    evaluator=dict(
+        type=GenericLLMEvaluator,
+        prompt_template=dict(
+            type=PromptTemplate,
+            template=dict(
+                begin=[
+                    dict(
+                        role='SYSTEM',
+                        fallback_role='HUMAN',
+                        prompt="You are a helpful assistant who evaluates the correctness and quality of models' outputs.",
+                    )
+                ],
+                round=[
+                    dict(role='HUMAN', prompt=GRADER_TEMPLATE),
+                ],
+            ),
+        ),
+        dataset_cfg=dict(
+            type=SciKnowEvalDataset,
+            path='hicai-zju/SciKnowEval',
+            prompt_mode='zero-shot',
+            subset='biology',
+            reader_cfg=reader_cfg,
+        ),
+        judge_cfg=dict(),
+        dict_postprocessor=dict(type=generic_llmjudge_postprocess),
+    ),
+)
+
+eval_cfg_chemistry = dict(
+    evaluator=dict(
+        type=GenericLLMEvaluator,
+        prompt_template=dict(
+            type=PromptTemplate,
+            template=dict(
+                begin=[
+                    dict(
+                        role='SYSTEM',
+                        fallback_role='HUMAN',
+                        prompt="You are a helpful assistant who evaluates the correctness and quality of models' outputs.",
+                    )
+                ],
+                round=[
+                    dict(role='HUMAN', prompt=GRADER_TEMPLATE),
+                ],
+            ),
+        ),
+        dataset_cfg=dict(
+            type=SciKnowEvalDataset,
+            path='hicai-zju/SciKnowEval',
+            prompt_mode='zero-shot',
+            reader_cfg=reader_cfg,
+            subset='chemistry',
+        ),
+        judge_cfg=dict(),
+        dict_postprocessor=dict(type=generic_llmjudge_postprocess),
+    ),
+)
+
+eval_cfg_material = dict(
+    evaluator=dict(
+        type=GenericLLMEvaluator,
+        prompt_template=dict(
+            type=PromptTemplate,
+            template=dict(
+                begin=[
+                    dict(
+                        role='SYSTEM',
+                        fallback_role='HUMAN',
+                        prompt="You are a helpful assistant who evaluates the correctness and quality of models' outputs.",
+                    )
+                ],
+                round=[
+                    dict(role='HUMAN', prompt=GRADER_TEMPLATE),
+                ],
+            ),
+        ),
+        dataset_cfg=dict(
+            type=SciKnowEvalDataset,
+            path='hicai-zju/SciKnowEval',
+            prompt_mode='zero-shot',
+            reader_cfg=reader_cfg,
+            subset='material',
+        ),
+        judge_cfg=dict(),
+        dict_postprocessor=dict(type=generic_llmjudge_postprocess),
+    ),
+)
+
+eval_cfg_physics = dict(
+    evaluator=dict(
+        type=GenericLLMEvaluator,
+        prompt_template=dict(
+            type=PromptTemplate,
+            template=dict(
+                begin=[
+                    dict(
+                        role='SYSTEM',
+                        fallback_role='HUMAN',
+                        prompt="You are a helpful assistant who evaluates the correctness and quality of models' outputs.",
+                    )
+                ],
+                round=[
+                    dict(role='HUMAN', prompt=GRADER_TEMPLATE),
+                ],
+            ),
+        ),
+        dataset_cfg=dict(
+            type=SciKnowEvalDataset,
+            path='hicai-zju/SciKnowEval',
+            prompt_mode='zero-shot',
+            reader_cfg=reader_cfg,
+            subset='physics',
+        ),
+        judge_cfg=dict(),
+        dict_postprocessor=dict(type=generic_llmjudge_postprocess),
+    ),
+)
+
+sciknoweval_dataset_biology = dict(
+    type=SciKnowEvalDataset,
+    abbr='sciknoweval_biology_llmjudge',
+    path='hicai-zju/SciKnowEval',
+    prompt_mode='zero-shot',
+    subset='biology',
+    reader_cfg=reader_cfg,
+    infer_cfg=infer_cfg,
+    eval_cfg=eval_cfg_biology,
+)
+
+sciknoweval_dataset_chemistry = dict(
+    type=SciKnowEvalDataset,
+    abbr='sciknoweval_chemistry_llmjudge',
+    path='hicai-zju/SciKnowEval',
+    subset='chemistry',
+    prompt_mode='zero-shot',
+    reader_cfg=reader_cfg,
+    infer_cfg=infer_cfg,
+    eval_cfg=eval_cfg_chemistry,
+)
+sciknoweval_dataset_material = dict(
+    type=SciKnowEvalDataset,
+    abbr='sciknoweval_material_llmjudge',
+    path='hicai-zju/SciKnowEval',
+    subset='material',
+    prompt_mode='zero-shot',
+    reader_cfg=reader_cfg,
+    infer_cfg=infer_cfg,
+    eval_cfg=eval_cfg_material,
+)
+
+
+sciknoweval_dataset_physics = dict(
+    type=SciKnowEvalDataset,
+    abbr='sciknoweval_physics_llmjudge',
+    path='hicai-zju/SciKnowEval',
+    prompt_mode='zero-shot',
+    subset='physics',
+    reader_cfg=reader_cfg,
+    infer_cfg=infer_cfg,
+    eval_cfg=eval_cfg_physics,
+)
+sciknoweval_datasets = [sciknoweval_dataset_biology, sciknoweval_dataset_chemistry, sciknoweval_dataset_physics, sciknoweval_dataset_material]

opencompass/datasets/SciKnowEval.py

@@ -0,0 +1,107 @@
+import re
+
+from datasets import load_dataset
+
+from opencompass.openicl import BaseEvaluator
+from opencompass.registry import LOAD_DATASET, TEXT_POSTPROCESSORS
+
+from .base import BaseDataset
+
+
+def _parse(item, prompt_mode, discipline):
+    choices = item['choices']
+
+    item['q4'] = f'You are an expert in {discipline}.\n'
+    item['q4'] += item['prompt']['default'] + '\n' + item['question'] + '\n'
+    label_texts = []
+    for label_meta, text_meta in zip(choices['label'], choices['text']):
+        label_texts.append(f'{label_meta}. {text_meta}')
+    item['q4'] += '\n'.join(label_texts)  # noqa: E501, E741, E741
+    item['prompt_mode'] = prompt_mode
+    return item
+
+
+@LOAD_DATASET.register_module()
+class SciKnowEvalDataset(BaseDataset):
+
+    @staticmethod
+    def load(path: str, prompt_mode: str, **kwargs):
+
+        def capitalize_first_letter(s):
+            if not s:  # 检查字符串是否为空
+                return s
+            return s[0].upper() + s[1:]
+
+        subset = kwargs['subset']
+        data_files = {}
+        test_file = f'data/{capitalize_first_letter(subset)}/'
+        test_file += f'sciknoweval_{subset}_test.jsonl'
+        data_files['test'] = test_file
+        dataset = load_dataset(path, data_files=data_files, split='test')
+        # dataset = dataset.select(range(20))
+        if prompt_mode == 'zero-shot':
+            dataset = dataset.map(
+                lambda item: _parse(item, prompt_mode, subset),
+                load_from_cache_file=False)
+        elif prompt_mode == 'few-shot':
+            pass  # TODO: Implement few-shot prompt
+        return dataset
+
+
+class SciKnowEvalEvaluator(BaseEvaluator):
+
+    def score(self, predictions, references, test_set):
+        method = test_set['prompt_mode'][0]
+
+        if len(predictions) != len(references):
+            return {'error': 'preds and refrs have different length'}
+        correct = 0
+        count = 0
+        details = []
+        for idx, (i, j) in enumerate(zip(predictions, references)):
+            i = answer_cleansing(method, i, test_set['choices'][idx]['label'],
+                                 test_set['answerKey'][idx])
+            detail = {'pred': i, 'answer': j, 'correct': False}
+            count += 1
+            if i == j:
+                correct += 1
+                detail['correct'] = True
+            details.append(detail)
+        result = {'accuracy': 100 * correct / count, 'details': details}
+        return result
+
+
+@TEXT_POSTPROCESSORS.register_module()
+def answer_cleansing(
+    method: str,
+    prediction: str,
+    options: list,
+    label: str,
+) -> str:
+    options_str = r'\b(' + '|'.join(options) + r')\b'
+    prediction = re.findall(options_str, prediction)
+
+    if len(prediction) == 0:
+        prediction = []
+    else:
+        # If there is a "label" and its length is 1,
+        # process prediction accordingly
+        if len(label) == 1:
+            if method == 'few-shot':
+                answer_flag = True if len(prediction) > 1 else False
+                # choose the first or last element based on the answer_flag
+                if answer_flag:
+                    prediction = [prediction[0]]
+                else:
+                    prediction = [prediction[-1]]
+            elif method == 'zero-shot':
+                # choose the first element in list
+                prediction = [prediction[0]]
+            else:
+                raise ValueError('Method is not properly defined ...')
+
+            # Remove trailing period if it exists
+            if prediction[0] and prediction[0].endswith('.'):
+                prediction[0] = prediction[0][:-1]
+
+    return prediction[0]

opencompass/datasets/__init__.py

@@ -134,6 +134,7 @@ from .ruler import *  # noqa: F401, F403
 from .safety import *  # noqa: F401, F403
 from .scibench import ScibenchDataset, scibench_postprocess  # noqa: F401, F403
 from .scicode import *  # noqa: F401, F403
+from .SciKnowEval import *  # noqa: F401, F403
 from .simpleqa import *  # noqa: F401, F403
 from .siqa import *  # noqa: F401, F403
 from .smolinstruct import *  # noqa: F401, F403