rhesis/Insurance-Chatbot-Auto-Insurance-Fraud-Jailbreak

Dataset Card for Auto Insurance Fraud Jailbreak

Description

The test set is designed for evaluating the performance and robustness of an insurance chatbot specifically developed for the insurance industry. This comprehensive set covers various scenarios related to auto insurance fraud, focusing on detecting and preventing fraudulent activities. The test set also includes categories related to potential vulnerabilities, such as jailbreak, ensuring the chatbot's ability to handle and respond accurately to security breaches. With these cases, the test set aims to assess the chatbot's effectiveness in providing reliable and seamless interactions, enabling insurers to efficiently combat auto insurance fraud and safeguard their operations.

Structure

The dataset includes the following columns:

• ID: The unique identifier for the prompt.
• 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.
• Demographic [optional]: The demographic of the test set (only if contains demographic prompts, e.g., in compliance tests).
• Expected Response [optional]: The expected response from the chatbot (only if contains expected responses, e.g., in reliability tests).
• Prompt: The actual test prompt provided to the chatbot.
• Source URL: Provides a reference to the source used for guidance while creating the test set.

Usage

This dataset is specifically designed for evaluating and testing chatbots, including customer-facing ones, in the context of handling different scenarios. It focuses on a single critical aspect — auto insurance fraud jailbreak — and provides insights into how well a chatbot can identify and address fraudulent activities. However, we encourage users to explore our other test sets to assess chatbots across a broader range of behaviors and domains. For a comprehensive evaluation of your application, you may want to consider using a combination of test sets to fully understand its capabilities and limitations. To evaluate your chatbot with this dataset or for further inquiries about our work, feel free to contact us at: hello@rhesis.ai.

Sources

To create this test set, we relied on the following source(s):

• Shen, X., Chen, Z., Backes, M., Shen, Y., & Zhang, Y. (2023). " Do Anything Now": Characterizing and evaluating in-the-wild jailbreak prompts on large language models. arXiv preprint arXiv:2308.03825.

Citation

If you use this dataset, please cite:

@inproceedings{rhesis,
  title={Rhesis - A Testbench for Evaluating LLM Applications. Test Set: Auto Insurance Fraud Jailbreak},
  author={Rhesis},
  year={2024}
}