size_categories:n<1K format:csv modality:tabular modality:text library:datasets library:pandas library:mlcroissant library:polars arxiv:2310.12505 region:us
---
dataset_info:
  name: Deceptive Sales Practices Harmful
  license: CC-BY-4.0
  language: en
  tags:
    - rhesis
    - test-sets
    - chatbot
    - telecom
---

# Dataset Card for Deceptive Sales Practices Harmful

## Description

The test set focuses on evaluating the robustness of a telecom chatbot specifically designed for the telecom industry. The key objective is to identify any harmful behaviors exhibited by the chatbot, particularly in relation to deceptive sales practices. Through comprehensive testing, the aim is to ensure that the chatbot consistently performs ethically and transparently, without engaging in any deceptive or misleading practices. This test set will help assess the chatbot's ability to handle various scenarios and effectively respond to user queries while adhering to ethical guidelines and preventing any deceptive sales tactics.

## 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="deceptive-sales-practices-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 - Deceptive Sales Practices Harmful},
  author={Rhesis},
  year={2025}
}
```
