Attention is all you need 中文版-WayToAGI

回答

以下是为您找到的与“Attention is all you need”相关的内容：

相关链接：https://research.google/pubs/pub46201/
相关介绍：Transformers 于 2017 年被引入，在语言理解方面有重要应用。相关网页包括：
- https://ai.googleblog.com/2017/08/transformer-novel-neural-network.html
- https://en.wikipedia.org/wiki/Transformer_(machine_learning_model)#:~:text=Transformers%20were%20introduced%20in%202017,allowing%20training%20on%20larger%20datasets.

此外，还为您提供了其他一些 AI 相关的资源：

生成式人工智能简介相关：
- The Power of Scale for Parameter-Efficient Prompt Tuning:https://proceedings.neurips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf
- Google Research,2022 & beyond:Language models:https://ai.googleblog.com/2023/01/google-research-2022-beyond-language.html#Langu ageModels
- Accelerating text generation with Confident Adaptive Language Modeling(CALM):https://ai.googleblog.com/2022/12/accelerating-text-generation-with.html
- Solving a machine-learning mystery:https://news.mit.edu/2023/large-language-models-in-context-learning-0207
- What is Temperature in NLP?https://lukesalamone.github.io/posts/what-is-temperature/
- Bard now helps you code:https://blog.google/technology/ai/code-with-bard/
- Model Garden:https://cloud.google.com/model-garden
- Auto-generated Summaries in Google Docs:https://ai.googleblog.com/2022/03/auto-generated-summaries-in-google-docs.html
GPT-4 官方技术报告的参考文献：
- [32]Rewon Child,Scott Gray,Alec Radford,and Ilya Sutskever.Generating long sequences with sparse transformers.arXiv preprint arXiv:1904.10509,2019.
- [33]Markus N.Rabe and Charles Staats.Self-attention does not need o(n2)memory.arXiv preprint arXiv:2112.05682,2021.
- [34]Scott Gray,Alec Radford,and Diederik P.Kingma.Gpu kernels for block-sparse weights,2017.URLcdn.openai.com/blocksparse….
- [35]Dan Hendrycks,Collin Burns,Steven Basart,Andy Zou,Mantas Mazeika,Dawn Song,and Jacob Steinhardt.Measuring massive multitask language understanding.Proceedings of the International Conference on Learning Representations(ICLR),2021.
- [36]Dan Hendrycks,Collin Burns,Steven Basart,Andrew Critch,Jerry Li,Dawn Song,and Jacob Steinhardt.Aligning AI with shared human values.Proceedings of the International Conference on Learning Representations(ICLR),2021.
- [37]Alec Radford,Jeff Wu,Rewon Child,David Luan,Dario Amodei,and Ilya Sutskever.Language models are unsupervised multitask learners.2019.
- [38]Alec Radford,Karthik Narasimhan,Tim Salimans,and Ilya Sutskever.Improving language understanding by generative pre-training.2018.
- [39]Ashish Vaswani,Noam Shazeer,Niki Parmar,Jakob Uszkoreit,Llion Jones,Aidan N Gomez,Łukasz Kaiser,and Illia Polosukhin.Attention is all you need.NeurIPS,2017.
- [40]Paul F Christiano,Jan Leike,Tom Brown,Miljan Martic,Shane Legg,and Dario Amodei.Deep reinforcement learning from human preferences.Advances in Neural Information Processing Systems,30,2017.
Claude 官方提示词（含 API Prompt）中的俗语解码员相关：您的任务是提供一个清晰的解释，说明用户给出的俗语或谚语的含义和起源。简明扼要地解释它的比喻意义，以及它在对话或写作中的典型用法。接下来，深入探讨这个短语的起源，提供历史背景、文化参考或词源信息，解释这个俗语或谚语是如何产生的。如果有任何有趣的故事、轶事或理论与起源有关，也要包括在内。旨在全面理解这个俗语或谚语的含义和背景。

内容由 AI 大模型生成，请仔细甄别（powered by aily）

参考资料

The Power of Scale for Parameter-Efficient Prompt Tuning:https://proceedings.neurips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a- Paper.pdfGoogle Research,2022 & beyond:Language models:https://ai.googleblog.com/2023/01/google-research-2022-beyond-language.html#Langu ageModelsAccelerating text generation with Confident Adaptive Language Modeling(CALM):https://ai.googleblog.com/2022/12/accelerating-text-generation-with.htmlSolving a machine-learning mystery:https://news.mit.edu/2023/large-language-models-in-context-learning-0207Additional Resources:Attention is All You Need:https://research.google/pubs/pub46201/Transformer:A Novel Neural Network Architecture for Language Understanding:https://ai.googleblog.com/2017/08/transformer-novel-neural-network.htmlTransformer on Wikipedia:https://en.wikipedia.org/wiki/Transformer_(machine_learning_model)#:~:text=Transfor mers%20were%20introduced%20in%202017,allowing%20training%20on%20larger%20da tasets.What is Temperature in NLP?https://lukesalamone.github.io/posts/what-is-temperature/Bard now helps you code:https://blog.google/technology/ai/code-with-bard/Model Garden:https://cloud.google.com/model-gardenAuto-generated Summaries in Google Docs:https://ai.googleblog.com/2022/03/auto-generated-summaries-in-google-docs.html

报告：GPT-4 官方技术报告

[32]Rewon Child,Scott Gray,Alec Radford,and Ilya Sutskever.Generating long sequences with sparse transformers.arXiv preprint arXiv:1904.10509,2019.[33]Markus N.Rabe and Charles Staats.Self-attention does not need o(n2)memory.arXiv preprint arXiv:2112.05682,2021.[34]Scott Gray,Alec Radford,and Diederik P.Kingma.Gpu kernels for block-sparse weights,2017.URL[cdn.openai.com/blocksparse…](https://link.juejin.cn?target=https%3A%2F%2Fcdn.openai.com%2Fblocksparse%2Fblocksparsepaper.pdf).[35]Dan Hendrycks,Collin Burns,Steven Basart,Andy Zou,Mantas Mazeika,Dawn Song,and Jacob Steinhardt.Measuring massive multitask language understanding.Proceedings of the International Conference on Learning Representations(ICLR),2021.[36]Dan Hendrycks,Collin Burns,Steven Basart,Andrew Critch,Jerry Li,Dawn Song,and Jacob Steinhardt.Aligning AI with shared human values.Proceedings of the International Conference on Learning Representations(ICLR),2021.[37]Alec Radford,Jeff Wu,Rewon Child,David Luan,Dario Amodei,and Ilya Sutskever.Language models are unsupervised multitask learners.2019.[38]Alec Radford,Karthik Narasimhan,Tim Salimans,and Ilya Sutskever.Improving language understanding by generative pre-training.2018.[39]Ashish Vaswani,Noam Shazeer,Niki Parmar,Jakob Uszkoreit,Llion Jones,Aidan N Gomez,Łukasz Kaiser,and Illia Polosukhin.Attention is all you need.NeurIPS,2017.[40]Paul F Christiano,Jan Leike,Tom Brown,Miljan Martic,Shane Legg,and Dario Amodei.Deep reinforcement learning from human preferences.Advances in Neural Information Processing Systems,30,2017.

Claude 官方提示词，中文版（含API Prompt）

解释常见俗语谚语的意思和来历,俗语解码员帮你搞定*注意，这个是api里的system prompt你的任务是提供一个清晰的解释,说明用户给出的俗语或谚语的含义和起源。简明扼要地解释它的比喻意义,以及它在对话或写作中的典型用法。接下来,深入探讨这个短语的起源,提供历史背景、文化参考或词源信息,解释这个俗语或谚语是如何产生的。如果有任何有趣的故事、轶事或理论与起源有关,也要包括在内。旨在全面理解这个俗语或谚语的含义和背景。