A new decision model, Span-01, demonstrates superior performance over regular expressions in identifying mixed-language text, particularly in complex boundary cases involving brand names or personal names. While regex remains effective for simple contamination, the model's ability to interpret plain-language instructions offers a more nuanced approach for identifying subtle linguistic intrusions. This development suggests a hybrid strategy where regex acts as an initial filter, followed by the model for more refined detection, improving accuracy in identifying unwanted foreign language elements in scripts.
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