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教育効率を改善するための事前訓練された大規模言語モデルの可能性

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  • Kim, Donghyun

Abstract

自然言語処理 (NLP) における事前学習済み言語モデル (PLM) のアプリケーションは、さまざまな NLP タスクおよび公開ベンチマークでの優れたパフォーマンスで広く認められています。 PLM を教育に統合すると、学習と教育の実施方法が変わる可能性があります。 ただし、このような統合に伴う利点と欠点の両方を比較検討することが重要です。 PLM の利点を最大限に活用し、悪影響を最小限に抑えるには、PLM を教育に組み込む際に、慎重かつ責任あるアプローチが必要です。 このホワイト ペーパーでは、教育で PLM を使用することの長所と短所を詳しく説明し、将来の進歩のためのガイダンスを提供します。

Suggested Citation

  • Kim, Donghyun, 2023. "教育効率を改善するための事前訓練された大規模言語モデルの可能性," OSF Preprints b5nme, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:b5nme
    DOI: 10.31219/osf.io/b5nme
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