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We describe PromptBoosting, a query-efficient procedure for building a text classifier from a neural language model (LM) without access to the LM's parameters, gradients, or hidden representations.
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Squad: 100,000+ questions for machine comprehension of text
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The power of scale for parameter-efficient prompt tuning
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Prefix-tuning: Optimizing continuous prompts for generation
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Liu, X., Zheng, Y., Du, Z., Ding, M., Qian, Y., Yang, Z., and Tang, J · 2021
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Learning how to ask: Querying lms with mixtures of soft prompts
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It’s not just size that matters: Small language models are also few-shot learners
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Bartscore: Evaluating generated text as text generation
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Language models are few-shot learners
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Differentiable prompt makes pre-trained language models better few-shot learners
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Clip-tuning: Towards derivative-free prompt learning with a mixture of rewards
Chai, Y., Wang, S., Sun, Y., Tian, H., Wu, H., and Wang, H · 2022
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Rlprompt: Optimizing discrete text prompts with reinforcement learning
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Black-box prompt learning for pre-trained language models
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P-tuning: Prompt tuning can be comparable to fine-tuning across scales and tasks
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Grips: Gradient-free, edit-based instruction search for prompting large language models
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Chain of thought prompting elicits reasoning in large language models
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Tempera: Test-time prompting via reinforcement learning
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