BLEU

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Overview

BLEU (Bilingual Evaluation Understudy) is a lexical-level eval that evaluates how many contiguous sequence of words (n-grams) in the generated text are also present in the reference text. It gives a numeric score between 0 and 1 quantifying how much the generated text looks like the reference text. Higher the score the more similar the generated text is to the reference text.

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n-gram

• An n-gram is contiguous sequence of “n” words in a row.
• For example, "The quick brown fox jumps over the lazy dog"
  • 1-grams (unigrams) are: "The", "quick", "brown", "fox"
  • 2-grams (bigrams) are: "The quick", "quick brown", "brown fox"
  • 3-grams (trigrams): "The quick brown", "quick brown fox"
  • … and so on

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Modified n-gram Precision

• To calculate precision, we can simply count up the number of generated text (unigrams) that appear in reference text and then divides them by the total number of words in generated text. This can be misleading especially when the generated text repeats words that happen to exist in the reference.
• Thats when modified n-gram precision comes in, which is the count of clipped matches divided by the total words in generated text. Clipping means restricting the count of each word in the generated text to the maximum number of times it appears in the reference.

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Calculation of BLEU Score

• For each n-gram, modified n-gram precision is calculated.

  $$P_1, P_2, \ldots, P_N$$

• To combine these individual scores, their geometric mean is taken. (Geometric mean is taken as it is more sensitive to imbalances than arithmetic mean as we want to penalise if the scores is low at any n-gram level)

• The geometric mean of these scores in log form is written as:

  $$\text{BLEU} = \exp\left( \sum_{n=1}^{N} w_n \cdot \log P_n \right)$$

• If the generated text is long enough or equal to the reference, BP = 1 (no penalty)

• If the generated text is too short, BP < 1 (penalises the score)

• So the final BLEU score comes out as:

  $$\text{BLEU} = BP \cdot \exp\left( \sum_{n=1}^{N} w_n \cdot \log P_n \right)$$

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BLEU Score Eval using Future AGI’s Python SDK

Click here to learn how to setup evaluation using the Python SDK.

Input & Configuration:

Parameter Options:

Output:

Example:

from fi.evals.metrics import BLEUScore
from fi.testcases import TestCase

test_case = TestCase(
    response="The quick brown fox jumps over the lazy dog",
    expected_text="The fast brown fox leaps over the sleepy dog"
)

bleu_evaluator = BLEUScore(config={
    "mode": "sentence",
    "max_n_gram": 2,
    "smooth": "method2"
})

result = bleu_evaluator.evaluate([test_case])
print(f"{result.eval_results[0].metrics[0].value:.4f}")

Output:

0.4714

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What if BLEU Score is Low?

• It could be due to the model generating correct meaning but different word ordering, thus reducing higher order n-gram precision. Try reducing max_n_gram in such case.

• Or it could be due to he generated text being shorter than the reference text, causing BP to reduce the score.

• Aggregate the BLEU score with semantic evals like Embedding Similarity using Aggregated Metric to have a holistic view of comparing generated text with reference text.

• Use different smooth method, such as smooth="method2" or smooth="method4" to mitigate the impact of zero matches at higher n-gram levels and obtain more stable scores for short or diverse outputs. Below is the guideline on what smoothing method to use based on different scenarios:

  Scenario	Suggested Smoothing Method
  Short outputs	method1 or method2
  High variance in phrasing	method4 or method5
  Very strict evaluation	method0 (no smoothing)
  General use	method1 (default) or method2 (balanced smoothing)
  Sparse references or low match rate (e.g., summaries)	method3
  Mixed-length outputs with partial n-gram match	method6
  when you want strictness early on, but flexibility only after the first break in match continuity.	method7