grouse 0.4.2

Evaluate Grounded Question Answering models and Grounded Question Answering evaluator models.

GroUSE

---

Evaluate Grounded Question Answering (GQA) models and GQA evaluator models. We implement the evaluation methods described in GroUSE: A Benchmark to Evaluate Evaluators in Grounded Question Answering.

• Install
• Command Line Usage
  • Evaluation of the Grounded Question Answering task
  • Unit Testing of Evaluators with GroUSE
  • Plot Matrices of unit tests success
• Python Usage
• Links
• Citation

Install

pip install grouse

Then, setup your OpenAI credentials by creating an .env file by copying the .env.dist file, filling in your OpenAI API key and organization id and exporting the environment variables export $(cat .env | xargs).

Command Line Usage

Evaluation of the Grounded Question Answering task

You can build a dataset in a jsonl file with the following format per line:

{
    "references": ["", ...], // List of references
    "input": "", // Query
    "actual_output": "", // Predicted answer generated by the model we want to evaluate
    "expected_output": "" // Ground truth answer to the input
}

You can also check this example example_data/grounded_qa.jsonl.

Then, run this command:

grouse evaluate {PATH_TO_DATASET_WITH_GENERATIONS} outputs/gpt-4o

We recommend using GPT-4 as an evaluator model as we optimised prompts for this model, but you can change the model and prompts using the otional arguments :

• --evaluator_model_name: Name of the evaluator model. It can be any LiteLLM model. The default model is GPT-4.
• --prompts_path: Path to the folder containing the prompts of the evaluator. By default, the prompts are those optimized for GPT-4.

Unit Testing of Evaluators with GroUSE

Meta-Evaluation consists in evaluating GQA evaluators with the GroUSE unit tests.

grouse meta-evaluate gpt-4o meta-outputs/gpt-4o

Optional arguments :

• --prompts_path: Path to the folder containing the prompts of the evaluator. By default, the prompts are those optimized for GPT-4.
• --train_set: Optional flag to meta-evaluate on the train set (16 tests) instead of the test set (144 tests). The train set is meant to be used during the prompt engineering phase.

Plot Matrices of unit tests success

You can plot the results of unit tests in the shape of matrices:

grouse plot meta-outputs/gpt-4o

The resulting matrices look like this:

Python Usage

from grouse import EvaluationSample, GroundedQAEvaluator

sample = EvaluationSample(
    input="What is the capital of France?",
    # Replace this with the actual output from your LLM application
    actual_output="The capital of France is Marseille.[1]",
    expected_output="The capital of France is Paris.[1]",
    references=["Paris is the capital of France."]
)
evaluator = GroundedQAEvaluator()
evaluator.evaluate([sample])

Tutorial

You can check this tutorial to get started on some examples.

Links

• Paper
• Unit Tests
• Finetuned model

Citation

@misc{muller2024grousebenchmarkevaluateevaluators,
      title={GroUSE: A Benchmark to Evaluate Evaluators in Grounded Question Answering}, 
      author={Sacha Muller and António Loison and Bilel Omrani and Gautier Viaud},
      year={2024},
      eprint={2409.06595},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2409.06595}, 
}