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@ -17,6 +17,7 @@ This guide contains a non-exhaustive set of learning guides and tools about prom
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- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://arxiv.org/abs/2107.13586)
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- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://arxiv.org/abs/2107.13586)
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- [A Taxonomy of Prompt Modifiers for Text-To-Image Generation](https://arxiv.org/abs/2204.13988)
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- [A Taxonomy of Prompt Modifiers for Text-To-Image Generation](https://arxiv.org/abs/2204.13988)
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- [Emergent Abilities of Large Language Models](https://arxiv.org/abs/2206.07682)
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- [Emergent Abilities of Large Language Models](https://arxiv.org/abs/2206.07682)
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- [A Survey for In-context Learning](https://arxiv.org/pdf/2301.00234.pdf)
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- Applications:
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- Applications:
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- [Legal Prompt Engineering for Multilingual Legal Judgement Prediction](https://arxiv.org/abs/2212.02199)
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- [Legal Prompt Engineering for Multilingual Legal Judgement Prediction](https://arxiv.org/abs/2212.02199)
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- [Investigating Prompt Engineering in Diffusion Models](https://arxiv.org/abs/2211.15462)
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- [Investigating Prompt Engineering in Diffusion Models](https://arxiv.org/abs/2211.15462)
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