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Prefix-Tuning: Optimizing Continuous Prompts for Generation

Proceedings of the 59th Annual Meeting of the Association for Computational Linguisticsand the 11th International Joint Conference on Natural Language Processing, pages 4582 4597 August 1 6, 2021. 2021 Association for Computational Linguistics4582 Prefix-Tuning: Optimizing Continuous Prompts for GenerationXiang Lisa LiStanford LiangStanford is the de facto way of leveraginglarge pretrained language models for down-stream tasks. However, fine-tuning modifiesall the language model parameters and there-fore necessitates storing a full copy for eachtask.

et al.,2021) simplifies our approach and applies it to T5 (Raffel et al.,2020), demonstrating that the performance gap between fine-tuning and p*-tuning vanishes as the model size grows. 3 Problem Statement Consider a conditional generation task where the input xis a context and the output yis a sequence of tokens. We focus on two tasks ...

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