mirror of
https://github.com/dair-ai/Prompt-Engineering-Guide
synced 2024-11-16 06:12:45 +00:00
Update README.md
This commit is contained in:
parent
b64d7325bc
commit
820a1461e1
@ -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)
|
- [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)
|
- [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)
|
||||||
|
- [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)
|
- [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)
|
||||||
- [PromptChainer: Chaining Large Language Model Prompts through Visual Programming](https://arxiv.org/abs/2203.06566)
|
- [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)
|
- [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:
|
- Collections:
|
||||||
- [Papers with Code](https://paperswithcode.com/task/prompt-engineering)
|
- [Papers with Code](https://paperswithcode.com/task/prompt-engineering)
|
||||||
- [Prompt Papers](https://github.com/thunlp/PromptPapers#papers)
|
- [Prompt Papers](https://github.com/thunlp/PromptPapers#papers)
|
||||||
|
Loading…
Reference in New Issue
Block a user