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Definition

The quasi-exact match test assesses if two strings are similar, allowing partial matches and variations while being more flexible than exact match.

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Taxonomy

  • Task types: LLM.
  • Availability: and .

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Why it matters

  • Quasi-exact match provides a more lenient evaluation than exact match, accounting for minor variations in formatting, spacing, or capitalization.
  • This metric is useful when you want to measure semantic correctness without being overly strict about formatting details.
  • It’s particularly valuable for tasks where the core content matters more than exact formatting, such as question answering or content generation.

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Required columns

To compute this metric, your dataset must contain the following columns:
  • Outputs: The generated text from your LLM
  • Ground truths: The reference/expected text to compare against

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Test configuration examples

If you are writing a tests.json, here are a few valid configurations for the quasi-exact match test: 
[
  {
    "name": "Mean quasi-exact match above 0.85",
    "description": "Ensure that the mean quasi-exact match score is above 0.85",
    "type": "performance",
    "subtype": "metricThreshold",
    "thresholds": [
      {
        "insightName": "metrics",
        "insightParameters": null,
        "measurement": "meanQuasiExactMatch",
        "operator": ">",
        "value": 0.85
      }
    ],
    "subpopulationFilters": null,
    "mode": "development",
    "usesValidationDataset": true,
    "usesTrainingDataset": false,
    "usesMlModel": false,
    "syncId": "b4dee7dc-4f15-48ca-a282-63e2c04e0689"
  }
]