UL2 is a versatile framework designed for training models that exhibit consistent effectiveness across various datasets and configurations. Leveraging Mixture-of-Denoisers (MoD), UL2 employs a pre-training objective that integrates diverse pre-training approaches, resulting in robust and adaptable models. By combining different paradigms, UL2 ensures that the trained models possess a broad understanding of language and perform well across different tasks and domains, making it a valuable tool for Natural Language Processing (NLP) applications.
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