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Faithfulness measures how grounded is the generated answer on the retrieved contexts.

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Deterministic metrics (read more)

Below is the list of deterministic metrics that measure the relationship between the generated answer and the retrieved contexts.
  • ROUGE-L precision measures the longest common subsequence between the generated answer and the retrieved contexts.
  • Token overlap precision calculates the precision of token overlap between the generated answer and the retrieved contexts.
  • BLEU (Bilingual Evaluation Understudy) calculates the n-gram precision. (Below: p_n is the n-gram precision, w_n is the weight for each n-gram, and BP is the brevity penalty to penalize short answers)
  • Rouge|Token Overlap|Bleu Faithfulness is defined as the proportion of the sentences in the generated answer that can matched to the retrieved context above a threshold.
Keywords AI defines the threshold as 0.5.

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LLM-Based metrics (read more)

Keywords AI prompts the LLM to calculate faithfulness based on classifying faithfulness by statement: classify_by_statement = TRUE where LLM is prompted to evaluate the faithfulness of each statement in the Generated Answer and outputs a float score:

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Settings and parameters

  1. Go to Keywords AI (on top of the left nav bar) > Evaluation > Text generation > Faithfulness
  1. Click on the Faithfulness card to create the setting:
  • Click the enable switch to turn on the evaluation
  • Pick which method you want to use:
    • LLM-based
    • ROUGE-L Precision
    • Token Overlap Precision
    • BLEU
  • Pick a LLM model you want to run the evaluation with (if you choose LLM-based method)
  • Hit the “Save” button.
  1. Make an API call, and the evaluation will be run based on the Ramdom sampling setting.
  2. Check results in the requests log