From d254252ed4f07b6ee96fc5aa7f7897767ff081d8 Mon Sep 17 00:00:00 2001 From: Elvis Saravia Date: Sun, 22 Jan 2023 17:12:42 -0600 Subject: [PATCH] Update README.md --- README.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/README.md b/README.md index d0d2207..a97851e 100644 --- a/README.md +++ b/README.md @@ -18,11 +18,13 @@ This guide contains a set of learning guides and tools related to prompt enginee - [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) - [A Survey for In-context Learning](https://arxiv.org/pdf/2301.00234.pdf) + - 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: - [Ask Me Anything: A simple strategy for prompting language models](https://paperswithcode.com/paper/ask-me-anything-a-simple-strategy-for) - [Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity](https://arxiv.org/abs/2104.08786) @@ -31,6 +33,7 @@ This guide contains a set of learning guides and tools related to prompt enginee - [BERTese: Learning to Speak to BERT](https://aclanthology.org/2021.eacl-main.316/) - [Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity](https://arxiv.org/abs/2104.08786) - [Large Language Models are Zero-Shot Reasoners](https://arxiv.org/abs/2205.11916) + - [Language Models are Few-Shot Learners](https://arxiv.org/abs/2005.14165) - [Structured Prompting: Scaling In-Context Learning to 1,000 Examples](https://arxiv.org/abs/2212.06713) - [Chain of Thought Prompting Elicits Reasoning in Large Language Models](https://arxiv.org/abs/2201.11903) - [Reframing Instructional Prompts to GPTk's Language](https://arxiv.org/abs/2109.07830)