Prefix-Tuning: Optimizing Continuous Prompts for Generation
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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Transformer-XL: Attentive Language Models beyond a Fixed ...
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