The dataset viewer is not available for this split.
Error code: FeaturesError Exception: ArrowInvalid Message: JSON parse error: Column() changed from object to string in row 0 Traceback: Traceback (most recent call last): File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 153, in _generate_tables df = pd.read_json(f, dtype_backend="pyarrow") File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 815, in read_json return json_reader.read() File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 1025, in read obj = self._get_object_parser(self.data) File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 1051, in _get_object_parser obj = FrameParser(json, **kwargs).parse() File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 1187, in parse self._parse() File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 1402, in _parse self.obj = DataFrame( File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/core/frame.py", line 778, in __init__ mgr = dict_to_mgr(data, index, columns, dtype=dtype, copy=copy, typ=manager) File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/core/internals/construction.py", line 503, in dict_to_mgr return arrays_to_mgr(arrays, columns, index, dtype=dtype, typ=typ, consolidate=copy) File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/core/internals/construction.py", line 114, in arrays_to_mgr index = _extract_index(arrays) File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/core/internals/construction.py", line 677, in _extract_index raise ValueError("All arrays must be of the same length") ValueError: All arrays must be of the same length During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 231, in compute_first_rows_from_streaming_response iterable_dataset = iterable_dataset._resolve_features() File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2643, in _resolve_features features = _infer_features_from_batch(self.with_format(None)._head()) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1659, in _head return _examples_to_batch(list(self.take(n))) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1816, in __iter__ for key, example in ex_iterable: File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1347, in __iter__ for key_example in islice(self.ex_iterable, self.n - ex_iterable_num_taken): File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 318, in __iter__ for key, pa_table in self.generate_tables_fn(**gen_kwags): File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 156, in _generate_tables raise e File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 130, in _generate_tables pa_table = paj.read_json( File "pyarrow/_json.pyx", line 308, in pyarrow._json.read_json File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: JSON parse error: Column() changed from object to string in row 0
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MMedS-Bench
The official benchmark for "Towards Evaluating and Building Versatile Large Language Models for Medicine".
Introduction
MedS-Bench is a comprehensive benchmark designed to assess the performance of various large language models (LLMs) in clinical settings. It extends beyond traditional multiple-choice questions to include a wider range of medical tasks, providing a robust framework for evaluating LLM capabilities in healthcare.
The benchmark is structured around 11 high-level clinical task categories, each derived from a collection of 28 existing datasets. These datasets have been reformatted into an instruction-prompted question-answering format, which includes hand-crafted task definitions to guide the LLM in generating responses. The categories included in MedS-Bench are diverse and cover essential aspects of clinical decision-making and data handling:
- Multi-choice Question Answering: Tests the ability of LLMs to select correct answers from multiple options based on clinical knowledge.
- Text Summarization: Assesses the capability to concisely summarize medical texts.
- Information Extraction: Evaluates how effectively an LLM can identify and extract relevant information from complex medical documents.
- Explanation and Rationale: Requires the model to provide detailed explanations or justifications for clinical decisions or data.
- Named Entity Recognition: Focuses on the ability to detect and classify entities within a medical text.
- Diagnosis: Tests diagnostic skills, requiring the LLM to identify diseases or conditions from symptoms and case histories.
- Treatment Planning: Involves generating appropriate treatment plans based on patient information.
- Clinical Outcome Prediction: Assesses the ability to predict patient outcomes based on clinical data.
- Text Classification: Involves categorizing text into predefined medical categories.
- Fact Verification: Tests the ability to verify the accuracy of medical facts.
- Natural Language Inference: Requires deducing logical relationships from medical text.
Notably, as the evaluation involves commercial models, for example, GPT-4 and Claude 3.5, it is extremely costly to adopt the original large-scale test split. Therefore, for some benchmarks, we randomly sampling a number of test cases. The cases used to reeproduce the results in the paper are in MedS-Bench-SPLIT. For more details, please refer to our paper。
Data Format
The data format is the same as MedS-Ins.
{
"Contributors": [""],
"Source": [""],
"URL": [""],
"Categories": [""],
"Reasoning": [""],
"Definition": [""],
"Input_language": [""],
"Output_language": [""],
"Instruction_language": [""],
"Domains": [""],
"Positive Examples": [ { "input": "", "output": "", "explanation": ""} ],
"Negative Examples": [ { "input": "", "output": "", "explanation": ""} ],
"Instances": [ { "id": "", "input": "", "output": [""]} ],
}
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