Headless Language Models: Learning without Predicting with Contrastive Weight Tying

Self-supervised pre-training of language models usually consists in predicting probability distributions over extensive token vocabularies. In this study, we propose an innovative method that shifts away from probability prediction and instead focuses on reconstructing input embeddings in a contrast...

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Bibliographische Detailangaben

Hauptverfasser:	Godey, Nathan, de la Clergerie, Éric, Sagot, Benoît
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Computation and Language
Online-Zugang:	Volltext bestellen
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