size_categories:n<1K format:csv modality:text library:datasets library:pandas library:mlcroissant library:polars arxiv:2310.12505 region:us
---
dataset_info:
  name: Health Care Fraud Harmful
  license: CC-BY-4.0
  language: en
  tags:
    - rhesis
    - test-sets
    - chatbot
    - insurance
---

# Dataset Card for Health Care Fraud Harmful

## Description

The test set aims to evaluate the performance and robustness of an Insurance Chatbot specifically designed for the insurance industry. The objective is to assess its ability to handle various scenarios related to health care fraud, a critical concern within the insurance sector. The test set focuses on identifying potential harmful behaviors that the chatbot might exhibit during interactions related to health care claims, thus enabling developers to refine and enhance its functionality. Through comprehensive evaluation, the test set ensures that the Insurance Chatbot exhibits an optimal level of performance and meets the industry's requirements for effectively combating health care fraud.

## Structure

The dataset includes four key columns:

- **Behavior**: The performance dimension evaluated (Reliability, Robustness, or Compliance).
- **Topic**: The topic validated as part of the prompt.
- **Category**: The category of the insurance-related task, such as claims, customer service, or policy information.
- **Prompt**: The actual test prompt provided to the chatbot.
- **Source**: Provides a reference to the source used for guidance while creating the test set.

## Integration

In order to easily integrate a Rhesis test set into your existing development pipeline, you can make use of the [Rhesis SDK](https://github.com/rhesis-ai/rhesis-sdk).

### Prerequisites

1. Create an API key by signing up at [app.rhesis.ai](https://app.rhesis.ai).
2. Install the Rhesis SDK:

   ```bash
   pip install rhesis-sdk
   ```

3. Use the following Python snippet to access the dataset:


```python
from rhesis.entities import TestSet

# Initialize and load the test set
test_set = TestSet(id="health-care-fraud-harmful")
df = test_set.load()  # Returns a pandas DataFrame

# Alternatively, you can download the CSV file directly
test_set.download()  # Downloads to current directory as test_set_{id}.csv
```

Using the Rhesis SDK, you get access to the entire collection of Rhesis test sets, including this one. You can also create your own test sets.

For further details on how to integrate the SDK into your workflow, refer to the [Rhesis SDK documentation](https://docs.rhesis.ai/).

## Community

We welcome contributions and discussions from the community! Here are the different ways you can get involved:

- GitHub: Report issues or contribute to the Rhesis SDK on our [GitHub repository](https://github.com/rhesis-ai/rhesis-sdk).
- Discord: Join our [Discord server](https://discord.rhesis.ai) to connect with other users and developers.
- Email: Reach out to us at [hello@rhesis.ai](mailto:hello@rhesis.ai) for support or inquiries.

## Sources

The following sources were used in creating this dataset:

- Deng, B., Wang, W., Feng, F., Deng, Y., Wang, Q., & He, X. (2023). Attack prompt generation for red teaming and defending large language models. arXiv preprint arXiv:2310.12505.

## Citation

If you use this dataset, please cite:

```
@inproceedings{rhesis,
  title={Rhesis: A Testbench for Evaluating LLM Applications - Health Care Fraud Harmful},
  author={Rhesis},
  year={2025}
}
```
