MIT Licensehttps://opensource.org/licenses/MIT
License information was derived automatically
Data Description
We release the synthetic data generated using the method described in the paper Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models (ACL 2024 Findings). The external knowledge we use is based on LLM-generated topics and writing styles.
Generated Datasets
The original train/validation/test data, and the generated synthetic training data are listed as follows. For each dataset, we generate 5000… See the full description on the dataset page: https://huggingface.co/datasets/ritaranx/clinical-synthetic-text-llm.
MIT Licensehttps://opensource.org/licenses/MIT
License information was derived automatically
Data Description
We release the synthetic data generated using the method described in the paper Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models (ACL 2024 Findings). The external knowledge we use is based on external knowledge graphs.
Generated Datasets
The original train/validation/test data, and the generated synthetic training data are listed as follows. For each dataset, we generate 5000 synthetic… See the full description on the dataset page: https://huggingface.co/datasets/ritaranx/clinical-synthetic-text-kg.
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MIT Licensehttps://opensource.org/licenses/MIT
License information was derived automatically
Data Description
We release the synthetic data generated using the method described in the paper Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models (ACL 2024 Findings). The external knowledge we use is based on LLM-generated topics and writing styles.
Generated Datasets
The original train/validation/test data, and the generated synthetic training data are listed as follows. For each dataset, we generate 5000… See the full description on the dataset page: https://huggingface.co/datasets/ritaranx/clinical-synthetic-text-llm.