A team needs to measure how closely a foundation model's machine-translated sentences match human reference translations. Which metric should they use for this evaluation?
Perplexity
BLEU
BERTScore
ROUGE
BLEU is designed for machine translation and scores overlap between candidate and reference translations using modified n-gram precision. ROUGE is optimized for evaluating text summarization by focusing on recall of overlapping units. BERTScore compares contextual embeddings to judge semantic similarity rather than exact translation overlap. Perplexity gauges how well a language model predicts the next token during training and is not suited for comparing generated translations with references.
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