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@ -39,12 +39,14 @@ This guide contains a set of learning guides and tools related to prompt enginee
- [Calibrate Before Use: Improving Few-Shot Performance of Language Models](https://arxiv.org/abs/2102.09690)
- [Reframing Instructional Prompts to GPTk's Language](https://arxiv.org/abs/2109.07830)
- [Promptagator: Few-shot Dense Retrieval From 8 Examples](https://arxiv.org/abs/2209.11755)
- [Teaching Algorithmic Reasoning via In-context Learning](https://arxiv.org/abs/2211.09066)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://arxiv.org/abs/2101.00190)
- [Making Pre-trained Language Models Better Few-shot Learners](https://aclanthology.org/2021.acl-long.295/)
- [Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm](https://www.arxiv-vanity.com/papers/2102.07350/)
- [A Taxonomy of Prompt Modifiers for Text-To-Image Generation](https://arxiv.org/abs/2204.13988)
- [PromptChainer: Chaining Large Language Model Prompts through Visual Programming](https://arxiv.org/abs/2203.06566)
- [How Can We Know What Language Models Know?](https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00324/96460/How-Can-We-Know-What-Language-Models-Know)
- Collections:
- [Papers with Code](https://paperswithcode.com/task/prompt-engineering)
- [Prompt Papers](https://github.com/thunlp/PromptPapers#papers)