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Prompt-Engineering-Guide
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@ -33,6 +33,7 @@ This guide contains a non-exhaustive set of learning guides and tools about prom
- [Chain of Thought Prompting Elicits Reasoning in Large Language Models](https://arxiv.org/abs/2201.11903) - [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) - [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) - [Promptagator: Few-shot Dense Retrieval From 8 Examples](https://arxiv.org/abs/2209.11755)
- [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/) - [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/) - [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) - [A Taxonomy of Prompt Modifiers for Text-To-Image Generation](https://arxiv.org/abs/2204.13988)