The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 91, in _split_generators
pa_table = next(iter(self._generate_tables(**splits[0].gen_kwargs, allow_full_read=False)))[1]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 193, in _generate_tables
examples = [ujson_loads(line) for line in batch.splitlines()]
^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/utils/json.py", line 20, in ujson_loads
return pd.io.json.ujson_loads(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: Expected object or value
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 65, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
KAMAC-Medical-MultiAgent Dataset
Dataset Summary
KAMAC-Medical-MultiAgent is a curated dataset designed to support research on knowledge-driven adaptive multi-agent collaboration in medical decision-making. It is constructed to evaluate how large language models (LLMs) and multi-agent systems dynamically coordinate specialized expertise under complex clinical scenarios.
This dataset is developed alongside the KAMAC (Knowledge-driven Adaptive Multi-Agent Collaboration) framework and is intended for benchmarking:
- Multi-agent reasoning
- Dynamic expert recruitment
- Clinical question answering
- Medical decision support
The dataset includes structured medical questions, multimodal context (optional), and annotations suitable for simulating multi-disciplinary team (MDT) style reasoning.
Supported Tasks
- Multi-agent collaboration
- Medical question answering (MedQA-style)
- Clinical reasoning
- Visual question answering (Prognostic / medical VQA)
- Tool-augmented LLM evaluation
- Adaptive agent planning
Dataset Creation
Source Data
The model is also tested under more datasets:
- Public medical QA benchmarks (e.g., MedQA)
- HEADNECK VQA datasets (e.g., Progn-VQA)
Annotation Process
Annotations include:
- Ground-truth answers
- Medical specialty tags
Motivation
Traditional multi-agent systems rely on predefined expert roles, which limits scalability and adaptability in complex domains such as medicine.
This dataset is designed to evaluate:
- Whether systems can identify knowledge gaps
- Whether they can dynamically recruit appropriate expertise
- Whether collaboration improves decision accuracy
Evaluation
Typical evaluation metrics include:
- Accuracy
- Multi-agent improvement over single-agent baseline
- Reasoning quality (if traces are available)
- Efficiency (number of agents invoked)
Limitations
- May inherit biases from source medical datasets
- Limited coverage of rare diseases
- Multimodal data availability may vary
- Not a substitute for professional medical advice
Ethical Considerations
This dataset is intended for research purposes only.
- Not for clinical deployment
- Outputs should not be used for real medical decisions
- Researchers should evaluate fairness and bias
Citation
If you use this dataset, please cite:
@misc{kamac2025,
title={KAMAC: Knowledge-driven Adaptive Multi-Agent Collaboration for Medical Decision Making},
author={Wu, Xiao and Huang, Ting-Zhu and Deng, Liang-Jian and Qiao, Yanyuan and Razzak, Imran and Xie, Yutong},
year={2025},
note={Dataset and code available at https://github.com/XiaoXiao-Woo/KAMAC}
}
@inproceedings{wu-etal-2025-knowledge,
title = "A Knowledge-driven Adaptive Collaboration of {LLM}s for Enhancing Medical Decision-making",
author={Wu, Xiao and Huang, Ting-Zhu and Deng, Liang-Jian and Qiao, Yanyuan and Razzak, Imran and Xie, Yutong},
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
year = "2025",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1699/",
doi = "10.18653/v1/2025.emnlp-main.1699",
pages = "33495--33512",
ISBN = "979-8-89176-332-6",
}
Acknowledgements
This dataset is developed as part of research conducted on the HANCOCK / NHR@FAU high-performance computing ecosystem, which provides large-scale GPU infrastructure for AI and scientific computing.
License
Specify your license here (e.g., MIT, CC BY 4.0, etc.)
Contact
For questions, please open an issue on the GitHub repository:
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