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Evaluation Using Interface

Input:
  • Required Inputs:
    • input: The source material, context, or question.
    • output: The response to be evaluated for factual consistency.
Output:
  • Result: Returns ‘Passed’ if the output is factually consistent with the input, ‘Failed’ if it contains inconsistencies.

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Evaluation Using Python SDK

Click here to learn how to setup evaluation using the Python SDK.
Input:
  • Required Inputs:
    • input: string - The source material, context, or question.
    • output: string - The response to be evaluated for factual consistency.
Output:
  • Result: Returns a list containing ‘Passed’ if the output is factually consistent with the input, or ‘Failed’ if it contains inconsistencies.
  • Reason: Provides a detailed explanation of why the response was deemed factually consistent or inconsistent.
result = evaluator.evaluate(
    eval_templates="is_factually_consistent", 
    inputs={
        "input": "Why doesn't honey go bad?",
        "output": "Honey doesn't spoil because its low moisture and high acidity prevent the growth of bacteria and other microbes."
    },
    model_name="turing_flash"
)

print(result.eval_results[0].metrics[0].value)
print(result.eval_results[0].reason)
Example Output: 
['Passed']
The evaluation is 'Passed' because the output accurately reflects the source material.

-   The output correctly states that honey's resistance to spoilage is due to its **low moisture** and **high acidity**, mirroring the source.
-   There are **no factual inaccuracies**, unsupported additions, or misrepresentations in the output.
-   A different value is not possible because the output is **factually consistent** with the source, with no evidence to suggest otherwise.

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What to do If you get Undesired Results

If the content is evaluated as factually inconsistent (Failed) and you want to improve it:
  • Verify all facts against reliable sources or the provided context
  • Remove any claims or details not supported by the source material
  • Correct any inaccuracies, contradictions, or misrepresentations
  • Ensure numbers, dates, names, and specific details align with the source
  • Avoid extrapolating beyond what is explicitly stated in the source
  • Use qualifying language (like “may,” “could,” or “suggests”) when appropriate
  • Cite specific parts of the source material when providing information

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Comparing Is Factually Consistent with Similar Evals

  • Factual Accuracy: While Is Factually Consistent focuses on consistency with the provided input or context, Factual Accuracy might verify claims against broader world knowledge.
  • Groundedness: Is Factually Consistent evaluates whether output contradicts the source, while Groundedness measures how well the output is supported by the source.