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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.

put is a textual description (e.g., “Starbucks serves coffee.”). Prefix-tuning prepends a sequence of continuous task-specific vectors to the input, which we call a prefix, depicted by red blocks in Figure1 (bottom). To generate each token, the LM can at-tend to the prefix as if it were a sequence of “virtual

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