From 692daa0fb2031a6d8f77b504de6527962de25896 Mon Sep 17 00:00:00 2001 From: Elvis Saravia Date: Fri, 16 Dec 2022 16:23:01 -0600 Subject: [PATCH] Update README.md --- README.md | 14 ++++++++++++-- 1 file changed, 12 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 3b0166e..5f0ca0c 100644 --- a/README.md +++ b/README.md @@ -4,8 +4,18 @@ This guide contains a non-exhaustive set of learning guides and tools about prom ## Papers -- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://arxiv.org/abs/2107.13586) -- [A Taxonomy of Prompt Modifiers for Text-To-Image Generation](https://arxiv.org/abs/2204.13988) +- Surveys: + - [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://arxiv.org/abs/2107.13586) + - [A Taxonomy of Prompt Modifiers for Text-To-Image Generation](https://arxiv.org/abs/2204.13988) +- Applications: + - [Legal Prompt Engineering for Multilingual Legal Judgement Prediction](https://arxiv.org/abs/2212.02199) + - [Investigating Prompt Engineering in Diffusion Models](https://arxiv.org/abs/2211.15462) + - [Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Language](https://arxiv.org/abs/2210.15157) + - [Piloting Copilot and Codex: Hot Temperature, Cold Prompts, or Black Magic?](https://arxiv.org/abs/2210.14699) +- Approaches/Techniques: + - [Large Language Models Are Human-Level Prompt Engineers](https://sites.google.com/view/automatic-prompt-engineer?pli=1) + - [Promptagator: Few-shot Dense Retrieval From 8 Examples](https://arxiv.org/abs/2209.11755) + ## Tools You can use the tools below to test out prompts and conduct research