提示工程 (Chinese Wikipedia)

Analysis of information sources in references of the Wikipedia article "提示工程" in Chinese language version.

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16web.archive.org

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9wikidata.org

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8arxiv.org

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6doi.org

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3medium.com

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1openai.com

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1aclanthology.org

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1neurips.cc

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1worldcat.org

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1semanticscholar.org

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1acm.org

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1cnet.com

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1openreview.net

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1googleblog.com

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1promptmuse.com

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1simonwillison.net

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1hackaday.com

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1theregister.com

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1nccgroup.com

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1arstechnica.com

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1washingtonpost.com

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1owasp.org

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1promptingguide.ai

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aclanthology.org

Brian Lester; Rami Al-Rfou; Noah Constant. The Power of Scale for Parameter-Efficient Prompt Tuning. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 2021-11: 3045–3059. arXiv:2104.08691 . doi:10.18653/V1/2021.EMNLP-MAIN.243. Wikidata Q110887400 （英语）.

acm.org

dl.acm.org

Liu, Vivian; Chilton, Lydia. Design Guidelines for Prompt Engineering Text-to-Image Generative Models. ACM Digital Library. Association for Computing Machinery. [2022-10-26]. （原始内容存档于2022-10-26）.

arstechnica.com

Edwards, Benj. AI-powered Bing Chat lost its mind when fed Ars Technica article. Ars Technica. 14 February 2023 [16 February 2023]. （原始内容存档于2023-02-22）（美国英语）.

arxiv.org

Pengfei Liu; Weizhe Yuan; Jinlan Fu; Zhengbao Jiang; Hiroaki Hayashi; Graham Neubig, Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing, 2021-07-28, arXiv:2107.13586 , Wikidata Q109286554 （英语）
Brian Lester; Rami Al-Rfou; Noah Constant. The Power of Scale for Parameter-Efficient Prompt Tuning. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 2021-11: 3045–3059. arXiv:2104.08691 . doi:10.18653/V1/2021.EMNLP-MAIN.243. Wikidata Q110887400 （英语）.
Tom Brown; Benjamin Mann; Nick Ryder; et al. Language Models are Few-Shot Learners. arXiv, Advances in Neural Information Processing Systems 33. Advances in Neural Information Processing Systems. 2020-05-28. ISSN 2331-8422. S2CID 218971783. arXiv:2005.14165 . doi:10.48550/ARXIV.2005.14165. Wikidata Q95727440 （英语）.
Victor Sanh; Albert Webson; Colin Raffel; et al, Multitask Prompted Training Enables Zero-Shot Task Generalization, 2021-10-15, arXiv:2110.08207 , Wikidata Q108941092 （英语）
Jason Wei; Xuezhi Wang; Dale Schuurmans; Maarten Bosma; Ed Chi; 黎曰国; Denny Zhou, Chain of Thought Prompting Elicits Reasoning in Large Language Models, 2022-01-28, arXiv:2201.11903 , doi:10.48550/ARXIV.2201.11903, Wikidata Q111971110 （英语）
Takeshi Kojima; Shixiang Shane Gu; Machel Reid; Yutaka Matsuo; Yusuke Iwasawa. Large Language Models are Zero-Shot Reasoners. Advances in Neural Information Processing Systems. 2022-05-24. ISBN 978-1-7138-7108-8. arXiv:2205.11916 . doi:10.48550/ARXIV.2205.11916. Wikidata Q112124882 （英语）. |journal=被忽略 (帮助)
Stephen H. Bach; Victor Sanh; Zheng-Xin Yong; et al, PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts, 2022-02-02, arXiv:2202.01279 , Wikidata Q110839490 （英语）
Wei, Jason; Wang, Xuezhi; Schuurmans, Dale; Bosma, Maarten; Ichter, Brian; Xia, Fei; Chi, Ed H.; Le, Quoc V.; Zhou, Denny. Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. 31 October 2022 [2023-06-12]. arXiv:2201.11903 . （原始内容存档于2023-06-07）（英语）.

cnet.com

McAuliffe, Zachary. Google's Latest AI Model Can Be Taught How to Solve Problems. CNET. [10 March 2023]. （原始内容存档于2023-05-26）（英语）.

doi.org

Xiang Lisa Li; Percy Liang. Prefix-Tuning: Optimizing Continuous Prompts for Generation. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021-08: 4582–4597. doi:10.18653/V1/2021.ACL-LONG.353. Wikidata Q110887424 （英语）.
Brian Lester; Rami Al-Rfou; Noah Constant. The Power of Scale for Parameter-Efficient Prompt Tuning. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 2021-11: 3045–3059. arXiv:2104.08691 . doi:10.18653/V1/2021.EMNLP-MAIN.243. Wikidata Q110887400 （英语）.
Tom Brown; Benjamin Mann; Nick Ryder; et al. Language Models are Few-Shot Learners. arXiv, Advances in Neural Information Processing Systems 33. Advances in Neural Information Processing Systems. 2020-05-28. ISSN 2331-8422. S2CID 218971783. arXiv:2005.14165 . doi:10.48550/ARXIV.2005.14165. Wikidata Q95727440 （英语）.
Jason Wei; Xuezhi Wang; Dale Schuurmans; Maarten Bosma; Ed Chi; 黎曰国; Denny Zhou, Chain of Thought Prompting Elicits Reasoning in Large Language Models, 2022-01-28, arXiv:2201.11903 , doi:10.48550/ARXIV.2201.11903, Wikidata Q111971110 （英语）
Takeshi Kojima; Shixiang Shane Gu; Machel Reid; Yutaka Matsuo; Yusuke Iwasawa. Large Language Models are Zero-Shot Reasoners. Advances in Neural Information Processing Systems. 2022-05-24. ISBN 978-1-7138-7108-8. arXiv:2205.11916 . doi:10.48550/ARXIV.2205.11916. Wikidata Q112124882 （英语）. |journal=被忽略 (帮助)

dx.doi.org

Xiang Lisa Li; Percy Liang. Prefix-Tuning: Optimizing Continuous Prompts for Generation. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021-08: 4582–4597. doi:10.18653/V1/2021.ACL-LONG.353. Wikidata Q110887424 （英语）.

googleblog.com

ai.googleblog.com

Wei, Jason; Zhou. Language Models Perform Reasoning via Chain of Thought. ai.googleblog.com. [10 March 2023]. （原始内容存档于2023-08-11）（英语）.

hackaday.com

Papp, Donald. What's Old Is New Again:. Hackaday. 2022-09-17 [2023-02-09]. （原始内容存档于2023-05-02）（美国英语）.

medium.com

Monge, Jim Clyde. Dall-E2 VS Stable Diffusion: Same Prompt, Different Results. MLearning.ai. 2022-08-25 [2022-08-31]. （原始内容存档于2022-08-26）（英语）.
Monge, Jim Clyde. Dall-E2 VS Stable Diffusion: Same Prompt, Different Results. MLearning.ai. 2022-08-25 [2022-08-31]. （原始内容存档于2022-08-26）（英语）.

cobusgreyling.medium.com

Prompt Chaining & Large Language Models. [2023-06-13]. （原始内容存档于2023-07-08）.

nccgroup.com

research.nccgroup.com

Selvi, Jose. 探索提示注入攻击. NCC集团研究. 2022-12-05 [2023-02-09]. （原始内容存档于2023-05-03）（美国英语）.

neurips.cc

proceedings.neurips.cc

Tom Brown; Benjamin Mann; Nick Ryder; et al. Language Models are Few-Shot Learners. arXiv, Advances in Neural Information Processing Systems 33. Advances in Neural Information Processing Systems. 2020-05-28. ISSN 2331-8422. S2CID 218971783. arXiv:2005.14165 . doi:10.48550/ARXIV.2005.14165. Wikidata Q95727440 （英语）.

openai.com

cdn.openai.com

Alec Radford; Jeffrey Wu; Rewon Child; David Luan; Dario Amodei; 伊爾亞·蘇茨克維, Language Models are Unsupervised Multitask Learners (PDF), 2019, Wikidata Q95726769 （英语）

openreview.net

Wei, Jason; Wang, Xuezhi; Schuurmans, Dale; Bosma, Maarten; Ichter, Brian; Xia, Fei; Chi, Ed H.; Le, Quoc V.; Zhou, Denny. Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. 31 October 2022 [2023-06-12]. arXiv:2201.11903 . （原始内容存档于2023-06-07）（英语）.

owasp.org

OWASP Top 10 for Large Language Model Applications. OWASP. （原始内容存档于2023-09-07）.

promptingguide.ai

对抗性提示. （原始内容存档于2023-06-11）.

promptmuse.com

Voiceflow: The Future of AI-Powered Conversational Interfaces. [2023-06-13]. （原始内容存档于2023-06-13）.

semanticscholar.org

api.semanticscholar.org

Tom Brown; Benjamin Mann; Nick Ryder; et al. Language Models are Few-Shot Learners. arXiv, Advances in Neural Information Processing Systems 33. Advances in Neural Information Processing Systems. 2020-05-28. ISSN 2331-8422. S2CID 218971783. arXiv:2005.14165 . doi:10.48550/ARXIV.2005.14165. Wikidata Q95727440 （英语）.

simonwillison.net

Willison, Simon. 针对GPT-3的提示注入攻击. simonwillison.net. 12 September 2022 [2023-02-09]. （原始内容存档于2023-05-03）（英国英语）.

theregister.com

Vigliarolo, Brandon. GPT-3 'prompt injection' attack causes bot bad manners. 19 September 2022 [2023-02-09]. （原始内容存档于2023-03-29）（英语）.

washingtonpost.com

将ChatGPT变成其邪恶双胞胎的巧妙技巧. Washington Post. 2023 [2023年2月16日]. （原始内容存档于2023年3月6日）.

web.archive.org

Liu, Vivian; Chilton, Lydia. Design Guidelines for Prompt Engineering Text-to-Image Generative Models. ACM Digital Library. Association for Computing Machinery. [2022-10-26]. （原始内容存档于2022-10-26）.
Monge, Jim Clyde. Dall-E2 VS Stable Diffusion: Same Prompt, Different Results. MLearning.ai. 2022-08-25 [2022-08-31]. （原始内容存档于2022-08-26）（英语）.
McAuliffe, Zachary. Google's Latest AI Model Can Be Taught How to Solve Problems. CNET. [10 March 2023]. （原始内容存档于2023-05-26）（英语）.
Wei, Jason; Wang, Xuezhi; Schuurmans, Dale; Bosma, Maarten; Ichter, Brian; Xia, Fei; Chi, Ed H.; Le, Quoc V.; Zhou, Denny. Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. 31 October 2022 [2023-06-12]. arXiv:2201.11903 . （原始内容存档于2023-06-07）（英语）.
Wei, Jason; Zhou. Language Models Perform Reasoning via Chain of Thought. ai.googleblog.com. [10 March 2023]. （原始内容存档于2023-08-11）（英语）.
Prompt Chaining & Large Language Models. [2023-06-13]. （原始内容存档于2023-07-08）.
Voiceflow: The Future of AI-Powered Conversational Interfaces. [2023-06-13]. （原始内容存档于2023-06-13）.
Monge, Jim Clyde. Dall-E2 VS Stable Diffusion: Same Prompt, Different Results. MLearning.ai. 2022-08-25 [2022-08-31]. （原始内容存档于2022-08-26）（英语）.
Willison, Simon. 针对GPT-3的提示注入攻击. simonwillison.net. 12 September 2022 [2023-02-09]. （原始内容存档于2023-05-03）（英国英语）.
Papp, Donald. What's Old Is New Again:. Hackaday. 2022-09-17 [2023-02-09]. （原始内容存档于2023-05-02）（美国英语）.
Vigliarolo, Brandon. GPT-3 'prompt injection' attack causes bot bad manners. 19 September 2022 [2023-02-09]. （原始内容存档于2023-03-29）（英语）.
Selvi, Jose. 探索提示注入攻击. NCC集团研究. 2022-12-05 [2023-02-09]. （原始内容存档于2023-05-03）（美国英语）.
Edwards, Benj. AI-powered Bing Chat lost its mind when fed Ars Technica article. Ars Technica. 14 February 2023 [16 February 2023]. （原始内容存档于2023-02-22）（美国英语）.
将ChatGPT变成其邪恶双胞胎的巧妙技巧. Washington Post. 2023 [2023年2月16日]. （原始内容存档于2023年3月6日）.
OWASP Top 10 for Large Language Model Applications. OWASP. （原始内容存档于2023-09-07）.
对抗性提示. （原始内容存档于2023-06-11）.

wikidata.org

Alec Radford; Jeffrey Wu; Rewon Child; David Luan; Dario Amodei; 伊爾亞·蘇茨克維, Language Models are Unsupervised Multitask Learners (PDF), 2019, Wikidata Q95726769 （英语）
Pengfei Liu; Weizhe Yuan; Jinlan Fu; Zhengbao Jiang; Hiroaki Hayashi; Graham Neubig, Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing, 2021-07-28, arXiv:2107.13586 , Wikidata Q109286554 （英语）
Xiang Lisa Li; Percy Liang. Prefix-Tuning: Optimizing Continuous Prompts for Generation. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021-08: 4582–4597. doi:10.18653/V1/2021.ACL-LONG.353. Wikidata Q110887424 （英语）.
Brian Lester; Rami Al-Rfou; Noah Constant. The Power of Scale for Parameter-Efficient Prompt Tuning. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 2021-11: 3045–3059. arXiv:2104.08691 . doi:10.18653/V1/2021.EMNLP-MAIN.243. Wikidata Q110887400 （英语）.
Tom Brown; Benjamin Mann; Nick Ryder; et al. Language Models are Few-Shot Learners. arXiv, Advances in Neural Information Processing Systems 33. Advances in Neural Information Processing Systems. 2020-05-28. ISSN 2331-8422. S2CID 218971783. arXiv:2005.14165 . doi:10.48550/ARXIV.2005.14165. Wikidata Q95727440 （英语）.
Victor Sanh; Albert Webson; Colin Raffel; et al, Multitask Prompted Training Enables Zero-Shot Task Generalization, 2021-10-15, arXiv:2110.08207 , Wikidata Q108941092 （英语）
Jason Wei; Xuezhi Wang; Dale Schuurmans; Maarten Bosma; Ed Chi; 黎曰国; Denny Zhou, Chain of Thought Prompting Elicits Reasoning in Large Language Models, 2022-01-28, arXiv:2201.11903 , doi:10.48550/ARXIV.2201.11903, Wikidata Q111971110 （英语）
Takeshi Kojima; Shixiang Shane Gu; Machel Reid; Yutaka Matsuo; Yusuke Iwasawa. Large Language Models are Zero-Shot Reasoners. Advances in Neural Information Processing Systems. 2022-05-24. ISBN 978-1-7138-7108-8. arXiv:2205.11916 . doi:10.48550/ARXIV.2205.11916. Wikidata Q112124882 （英语）. |journal=被忽略 (帮助)
Stephen H. Bach; Victor Sanh; Zheng-Xin Yong; et al, PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts, 2022-02-02, arXiv:2202.01279 , Wikidata Q110839490 （英语）

worldcat.org

Tom Brown; Benjamin Mann; Nick Ryder; et al. Language Models are Few-Shot Learners. arXiv, Advances in Neural Information Processing Systems 33. Advances in Neural Information Processing Systems. 2020-05-28. ISSN 2331-8422. S2CID 218971783. arXiv:2005.14165 . doi:10.48550/ARXIV.2005.14165. Wikidata Q95727440 （英语）.