Update README.md

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