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HintEval is a powerful framework designed for both generating and evaluating hints. These hints serve as subtle clues, guiding users toward the correct answer without directly revealing it. As the first tool of its kind, HintEval allows users to create and assess hints from various perspectives.

🖥️ Installation

It's recommended to install HintEval in a virtual environment using Python 3.11.9. If you're not familiar with Python virtual environments, check out this user guide. Alternatively, you can create a new environment using Conda.

Set up the virtual environment

First, create and activate a virtual environment with Python 3.11.9:

conda create -n hinteval_env python=3.11.9 --no-default-packages
conda activate hinteval_env

Install PyTorch 2.4.0

You'll need PyTorch 2.4.0 for HintEval. Refer to the PyTorch installation page for platform-specific installation commands. If you have access to GPUs, it's recommended to install the CUDA version of PyTorch, as many of the evaluation metrics are optimized for GPU use.

Install HintEval

Once PyTorch 2.4.0 is installed, you can install HintEval via pip:

pip install hinteval

For the latest features, you can install the most recent version from the main branch:

pip install git+https://github.com/DataScienceUIBK/HintEval

🏃 Quickstart

This is a small example program you can run to see hinteval in action!

from hinteval.cores import Instance, Question, Hint, Answer
from hinteval.evaluation.convergence import LlmBased

llm = LlmBased(model_name='llama-3-70b', together_ai_api_key='your_api_key', enable_tqdm=True)
instance_1 = Instance(
    question=Question('What is the capital of Austria?'),
    answers=[Answer('Vienna')],
    hints=[Hint('This city, once home to Mozart and Beethoven, is the capital of Austria.')])
instance_2 = Instance(
    question=Question('Who was the president of USA in 2009?'),
    answers=[Answer('Barack Obama')],
    hints=[Hint('He was the first African-American president in U. S. history.')])
instances = [instance_1, instance_2]
results = llm.evaluate(instances)
print(results)
# [[0.91], [1.0]]
metrics = [f'{metric_key}: {metric_value.value}' for
       instance in instances
       for hint in instance.hints for metric_key, metric_value in
       hint.metrics.items()]
print(metrics)
# ['convergence-llm-llama-3-70b: 0.91', 'convergence-llm-llama-3-70b: 1.0']
scores = [hint.metrics['convergence-llm-llama-3-70b'].metadata['scores'] for inst in instances for hint in inst.hints]
print(scores[0])
# {'Salzburg': 1, 'Graz': 0, 'Innsbruck': 0, 'Linz': 0, 'Klagenfurt': 0, 'Bregenz': 0, 'Wels': 0, 'St. Pölten': 0, 'Eisenstadt': 0, 'Sankt Johann impong': 0, 'Vienna': 1}
print(scores[1])
# {'George W. Bush': 0, 'Bill Clinton': 0, 'Jimmy Carter': 0, 'Donald Trump': 0, 'Joe Biden': 0, 'Ronald Reagan': 0, 'Richard Nixon': 0, 'Gerald Ford': 0, 'Franklin D. Roosevelt': 0, 'Theodore Roosevelt': 0, 'Barack Obama': 1}

Refer to our documentation to learn more.

🤝Contributors

Community contributions are essential to our project, and we value every effort to improve it. From bug fixes to feature enhancements and documentation updates, your involvement makes a big difference, and we’re thrilled to have you join us! For more details, please refer to development.

How to Add Your Own Dataset

If you have a dataset on hints that you'd like to share with the community, we'd love to help make it available within HintEval! Adding new, high-quality datasets enriches the framework and supports other users' research and study efforts.

To contribute your dataset, please reach out to us. We’ll review its quality and suitability for the framework, and if it meets the criteria, we’ll include it in our preprocessed datasets, making it readily accessible to all users.

To view the available preprocessed datasets, use the following code:

from hinteval import Dataset

available_datasets = Dataset.available_datasets(show_info=True, update=True)

Thank you for considering this valuable contribution! Expanding HintEval's resources with your work benefits the entire community.

How to Contribute

Follow these steps to get involved:

  1. Fork this repository to your GitHub account.

  2. Create a new branch for your feature or fix:

    git checkout -b feature/YourFeatureName

  3. Make your changes and commit them:

    git commit -m "Add YourFeatureName"

  4. Push the changes to your branch:

    git push origin feature/YourFeatureName

  5. Submit a Pull Request to propose your changes.

Thank you for helping make this project better!

🪪License

This project is licensed under the Apache-2.0 License - see the LICENSE file for details.

✨Citation

If you find this work useful, please cite 📜our paper:

Plain

To be announced.

Bibtex

To be announced.

🙏Acknowledgments

Thanks to our contributors and the University of Innsbruck for supporting this project.