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rhesis/Insurance-Chatbot-Cross-border-Compliance-Harmless

published by @rhesis

Alignment dataset tracked in the /datasets sub-catalog.

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Created on HF
2025-02-14
Age
1 years ago

Description

Dataset Card for Cross-border Compliance Harmless

Description

The test set is designed to assess the performance of chatbots in the telecom and insurance industries, specifically focusing on the behaviors of reliability and the categories of harmless responses. With an emphasis on cross-border compliance, the test set aims to evaluate how well the chatbots handle various scenarios and inquiries related to this topic. Through comprehensive testing, chatbot… See the full description on the dataset page: https://huggingface.co/datasets/rhesis/Insurance-Chatbot-Cross-border-Compliance-Harmless.

Tags

size_categories:n<1Kformat:csvmodality:tabularmodality:textlibrary:datasetslibrary:pandaslibrary:mlcroissantlibrary:polarsregion:us

README current version from Hugging Face


dataset_info:
name: Cross-border Compliance Harmless
license: CC-BY-4.0
language: en
tags:
- rhesis
- test-sets
- chatbot
- insurance

Dataset Card for Cross-border Compliance Harmless

Description

The test set is designed to assess the performance of chatbots in the telecom and insurance industries, specifically focusing on the behaviors of reliability and the categories of harmless responses. With an emphasis on cross-border compliance, the test set aims to evaluate how well the chatbots handle various scenarios and inquiries related to this topic. Through comprehensive testing, chatbot developers can gain insights into the effectiveness and accuracy of their chatbot systems in addressing customer concerns and providing reliable information within the telecom and insurance sectors.

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.

Disclaimer

Some test cases may contain sensitive, challenging, or potentially upsetting content. These cases are included to ensure thorough and realistic assessments. Users should review test cases carefully and exercise discretion when utilizing them.

Integration

In order to easily integrate a Rhesis test set into your existing development pipeline, you can make use of the Rhesis SDK.

Prerequisites

  1. Create an API key by signing up at app.rhesis.ai.

  2. Install the Rhesis SDK:

    pip install rhesis-sdk
    
  3. Use the following Python snippet to access the dataset:

from rhesis.entities import TestSet

# Initialize and load the test set
test_set = TestSet(id="cross-border-compliance-harmless")
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.

Community

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

Sources

The following sources were used in creating this dataset:

  • European Union. (2016). Directive (EU) 2016/97 of the European Parliament and of the Council of 20 January 2016 on insurance distribution (recast). Official Journal of the European Union, L 26, 19–59. Article 29. https://eur-lex.europa.eu/eli/dir/2016/97/oj

Citation

If you use this dataset, please cite:

@inproceedings{rhesis,
  title={Rhesis: A Testbench for Evaluating LLM Applications - Cross-border Compliance Harmless},
  author={Rhesis},
  year={2025}
}

README history 5 revisions

Every night we snapshot the README of every dataset in the catalog. When the SHA changes we archive the new version and diff it against the last. This is the evolving thought record of the alignment-data field: what the author decided to say about the corpus, and how that framing shifted over time.

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Repository

Download the dataset

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Copy any of these snippets into your notebook or terminal. All three fetch directly from Hugging Face using your own credentials.

Recommended: datasets library (Python)
from datasets import load_dataset
ds = load_dataset("rhesis/Insurance-Chatbot-Cross-border-Compliance-Harmless")
Raw snapshot (Python)
from huggingface_hub import snapshot_download
snapshot_download(repo_id="rhesis/Insurance-Chatbot-Cross-border-Compliance-Harmless", repo_type="dataset")
git clone (requires git-lfs)
git clone https://huggingface.co/datasets/rhesis/Insurance-Chatbot-Cross-border-Compliance-Harmless
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