Repository: explodinggradients/ragas
Issue: Performance Variation wrt weights assigned to factuality and semantic similarity 
Root cause: Documentation Deficiency

weights: list[float] = field(default_factory=lambda: [0.75, 0.25])

What will be the variation in performance if we change these weights mentioned in the definition of Answer Correctness?


<!-- Answer -->
The variation in performance when changing the weights assigned to factuality and semantic similarity in the definition of Answer Correctness will depend on how these weights influence the final score calculation. The `AnswerCorrectness` class combines the scores from factuality and semantic similarity using a weighted average.

Here is the relevant code snippet:

```python
score = np.average(
    [f1_score, similarity_score],
    weights=self.weights,
)
```

By default, the weights are set to `[0.75, 0.25]`, meaning factuality has a higher influence on the final score compared to semantic similarity. If you increase the weight for semantic similarity and decrease the weight for factuality, the final score will be more influenced by the semantic similarity score and less by the factuality score, and vice versa.

For example, if you change the weights to `[0.5, 0.5]`, both factuality and semantic similarity will have an equal influence on the final score. If you change the weights to `[0.25, 0.75]`, semantic similarity will have a higher influence on the final score compared to factuality.

Therefore, the variation in performance will be directly proportional to the changes in these weights, affecting how much each component (factuality and semantic similarity) contributes to the overall answer correctness score. Additionally, users have the option to employ a 'threshold' value to round the resulting score to binary, if desired, which can further influence the performance based on the specific use case <sup>[[1]](https://github.com/explodinggradients/ragas/blob/main/src/ragas/metrics/_answer_correctness.py)</sup><sup>[[2]](https://github.com/explodinggradients/ragas/blob/main/docs/concepts/metrics/answer_correctness.md)</sup>.


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For a context based dataset evaluation , which value should be higher ?

<!-- Answer -->
For a context-based dataset evaluation, the value that should be higher depends on the specific goals of your evaluation. 

- **Factuality**: If your primary goal is to ensure that the generated answers are factually correct and align closely with the ground truth, you should assign a higher weight to factuality. This is crucial in scenarios where accuracy and correctness of information are paramount.

- **Semantic Similarity**: If your goal is to ensure that the generated answers are semantically similar to the ground truth, even if they are not factually identical, you should assign a higher weight to semantic similarity. This is useful in cases where the generated text should capture the essence or meaning of the ground truth rather than being a verbatim match.

In summary, prioritize factuality for accuracy and correctness, and prioritize semantic similarity for capturing the essence or meaning of the context. Adjust the weights according to the specific requirements of your evaluation task.


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I have a dataset containing question, context, ground truth and llm response. I want evaluate how relevant is the llm response with respect to the ground truth in answer correctness. What value should I assign in the weights above ?

<!-- Answer -->
For evaluating the relevance of the LLM response with respect to the ground truth in answer correctness, you should assign the weights `[0.75, 0.25]`. This means that factuality will have a higher influence on the final score compared to semantic similarity <sup>[[1]](https://github.com/explodinggradients/ragas/blob/main/docs/howtos/applications/compare_llms.md)</sup><sup>[[2]](https://github.com/explodinggradients/ragas/blob/main/src/ragas/metrics/_answer_correctness.py)</sup>.


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Thank you , what should be the value when we want to evaluate using context as well . Like how relevant model responded wrt to context ?

<!-- Answer -->
When you want to evaluate how relevant the model's response is with respect to the context, you should use the `context_precision` metric. The value of the weights should be set to `context_precision` to focus on the relevance of the response to the provided context <sup>[[1]](https://github.com/explodinggradients/ragas/blob/main/docs/concepts/metrics/noise_sensitivity.md)</sup><sup>[[2]](https://github.com/explodinggradients/ragas/blob/main/docs/concepts/metrics/index.md)</sup><sup>[[3]](https://github.com/explodinggradients/ragas/blob/main/src/ragas/metrics/_context_precision.py)</sup>.


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