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Definition

The BLEU (Bilingual Evaluation Understudy) score test measures the quality of machine-generated text by comparing it to reference text. BLEU scores are available for unigram to 4-gram precision (BLEU-1, BLEU-2, BLEU-3, and BLEU-4).

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Taxonomy

  • Task types: LLM.
  • Availability: and .

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Why it matters

  • BLEU scores provide a standardized way to evaluate the quality of generated text against reference translations or expected outputs.
  • Different n-gram levels capture different aspects of text quality: BLEU-1 focuses on word choice, while higher n-grams (BLEU-2 to BLEU-4) capture phrase structure and fluency.
  • This metric is particularly useful for translation tasks, text summarization, and other text generation applications where you have reference outputs.

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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 BLEU score test: 
[
  {
    "name": "Mean BLEU-1 score above 0.6",
    "description": "Ensure that the mean BLEU-1 score is above 0.6",
    "type": "performance",
    "subtype": "metricThreshold",
    "thresholds": [
      {
        "insightName": "metrics",
        "insightParameters": null,
        "measurement": "meanBleu1",
        "operator": ">",
        "value": 0.6
      }
    ],
    "subpopulationFilters": null,
    "mode": "development",
    "usesValidationDataset": true,
    "usesTrainingDataset": false,
    "usesMlModel": false,
    "syncId": "b4dee7dc-4f15-48ca-a282-63e2c04e0689"
  }
]

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Available measurements

  • meanBleu1 - Mean BLEU-1 score (unigram precision)
  • meanBleu2 - Mean BLEU-2 score (bigram precision)
  • meanBleu3 - Mean BLEU-3 score (trigram precision)
  • meanBleu4 - Mean BLEU-4 score (4-gram precision)