The correct choice is Fine-tune the model with locale-specific training data. Fine tuning on curated subtitle corpora for each region teaches idioms and the preferred tone so translations feel natural to local viewers.

By applying fine-tuning the model learns recurring regional expressions and style from examples so it can generate consistent idiomatic phrasing rather than only literal translations. This approach lets you control register and cultural references by including annotated, locale specific examples during training.

Temperature adjustment only changes output randomness and variety and does not teach the model regional idioms or a consistent tone. It will not reliably produce culturally appropriate phrasing.

BLEU score optimization is a metric and a training objective that measures overlap with references. It does not itself add idiomatic style or cultural nuance to outputs.

Knowledge Bases for Amazon Bedrock can provide factual context via retrieval but they do not inherently change the model s language style or introduce idiomatic local phrasing unless you also customize the model with locale specific data.

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