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Author

Halim Wildan Awalurahman

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Open access Jul 2026

Multi-task fine-tuning using prefix-prepend format in text-to-text transfer transformer for automatic question generation in Bahasa Indonesia

A prefix-prepend format for fine-tuning with the idT5-base model on the Indonesian Stanford Question Answering Dataset (SQuAD) and the Typologically Diverse Question Answering (TyDiQA) dataset is proposed and shows that prefix-prepend fine-tuning improved the performance of the baseline model.

Halim Wildan Awalurahman, Indra Budi · 0 citations

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