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The Text Similarity metric is one of the Deterministic Metric options provided by Galtea. It evaluates how closely a generated text resembles a reference by using fuzzy string matching techniques. This approach is especially valuable in scenarios where flexible wording, paraphrasing, or non-exact phrasing is acceptable, but a general sense of alignment is still important.

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Evaluation Parameters

To compute the text_similarity metric, the following parameters must be provided:
  • actual_output: The generated text or summary from the model.
  • expected_output: The reference or ground truth text to compare against.

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How Is It Calculated?

The metric uses a fuzzy string matching algorithm, which compares the characters in both strings and calculates how many edits (insertions, deletions, or substitutions) are needed to transform one into the other.

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Interpretation of Scores

  • ≥ 0.8 – High textual similarity; close match.
  • 0.5 – 0.8 – Moderate similarity; captures most content.
  • < 0.5 – Low similarity; outputs may diverge significantly.

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Suggested Test Case Types

Use Text Similarity when evaluating:
  • General text matching where loose phrasing is acceptable.
  • Heuristic checks for paraphrasing or alternative wording.
  • Lightweight similarity evaluations without requiring semantic understanding.