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@ -14,16 +14,16 @@ This guide contains a set of papers, learning guides, and tools related to promp
## Papers ## Papers
- Surveys / Overviews: - Surveys / Overviews:
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://arxiv.org/abs/2107.13586) - [A Survey for In-context Learning](https://arxiv.org/abs/2301.00234) (Dec 2022)
- [A Taxonomy of Prompt Modifiers for Text-To-Image Generation](https://arxiv.org/abs/2204.13988) - [Emergent Abilities of Large Language Models](https://arxiv.org/abs/2206.07682) (Jun 2022)
- [Emergent Abilities of Large Language Models](https://arxiv.org/abs/2206.07682) - [A Taxonomy of Prompt Modifiers for Text-To-Image Generation](https://arxiv.org/abs/2204.13988) (Apr 2022)
- [A Survey for In-context Learning](https://arxiv.org/pdf/2301.00234.pdf) - [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://arxiv.org/abs/2107.13586) (Jul 2021)
- Applications: - Applications:
- [Legal Prompt Engineering for Multilingual Legal Judgement Prediction](https://arxiv.org/abs/2212.02199) - [Legal Prompt Engineering for Multilingual Legal Judgement Prediction](https://arxiv.org/abs/2212.02199) (Dec 2022)
- [Investigating Prompt Engineering in Diffusion Models](https://arxiv.org/abs/2211.15462) - [Investigating Prompt Engineering in Diffusion Models](https://arxiv.org/abs/2211.15462) (Nov 2022)
- [Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Language](https://arxiv.org/abs/2210.15157) - [Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Language](https://arxiv.org/abs/2210.15157) (Oct 2022)
- [Piloting Copilot and Codex: Hot Temperature, Cold Prompts, or Black Magic?](https://arxiv.org/abs/2210.14699) - [Piloting Copilot and Codex: Hot Temperature, Cold Prompts, or Black Magic?](https://arxiv.org/abs/2210.14699) (Oct 2022)
- Approaches/Techniques: - Approaches/Techniques:
- [Batch Prompting: Efficient Inference with LLM APIs](https://arxiv.org/abs/2301.08721) (Jan 2023) - [Batch Prompting: Efficient Inference with LLM APIs](https://arxiv.org/abs/2301.08721) (Jan 2023)