📚 Bibliography

The page contains an organized list of all papers used by this course. The papers are organized by topic.

To cite this course, use the provided citation in the Github repository.

@software{Schulhoff_Learn_Prompting_2022,
    author = {Schulhoff, Sander and Community Contributors},
    month = dec,
    title = {{Learn Prompting}},
    url = {https://github.com/trigaten/Learn_Prompting},
    year = {2022}
}

Note: since neither the GPT-3 nor the GPT-3 Instruct paper correspond to davinci models, I attempt not to cite them as such.

Agents

MRKL¹

ReAct²

PAL³

Auto-GPT⁴

Baby AGI⁵

AgentGPT⁶

Toolformer⁷

Automated

AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts⁸

automatic prompt engineer⁹

Soft Prompting¹⁰

discretized soft prompting (interpreting)¹¹

Datasets

SCAN dataset (compositional generalization)¹²

GSM8K¹³

hotpotQA¹⁴

multiarith¹⁵

fever dataset¹⁶

bbq¹⁷

Detection

Don't ban chatgpt in schools. teach with it.¹⁸

Schools Shouldn't Ban Access to ChatGPT¹⁹

Certified Neural Network Watermarks with Randomized Smoothing²⁰

Watermarking Pre-trained Language Models with Backdooring²¹

GW preparing disciplinary response to AI programs as faculty explore educational use²²

A Watermark for Large Language Models²³

DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature²⁴

Image Prompt Engineering

Prompt Engineering for Text-Based Generative Art²⁵

The DALLE 2 Prompt Book²⁶

With the right prompt, Stable Diffusion 2.0 can do hands.²⁷

Meta Analysis

How Generative AI Is Changing Creative Work²⁸

How AI Will Change the Workplace²⁹

ChatGPT took their jobs. Now they walk dogs and fix air conditioners.³⁰

No title³¹

Miscl

The Turking Test: Can Language Models Understand Instructions?³²

A Taxonomy of Prompt Modifiers for Text-To-Image Generation³³

DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Models³⁴

Optimizing Prompts for Text-to-Image Generation³⁵

Language Model Cascades³⁶

Design Guidelines for Prompt Engineering Text-to-Image Generative Models³⁷

Discovering Language Model Behaviors with Model-Written Evaluations³⁸

Selective Annotation Makes Language Models Better Few-Shot Learners³⁹

Atlas: Few-shot Learning with Retrieval Augmented Language Models⁴⁰

STRUDEL: Structured Dialogue Summarization for Dialogue Comprehension⁴¹

Prompting Is Programming: A Query Language For Large Language Models⁴²

Parallel Context Windows Improve In-Context Learning of Large Language Models⁴³

Learning to Perform Complex Tasks through Compositional Fine-Tuning of Language Models⁴⁴

Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks⁴⁵

Making Pre-trained Language Models Better Few-shot Learners⁴⁶

How to Prompt? Opportunities and Challenges of Zero- and Few-Shot Learning for Human-AI Interaction in Creative Applications of Generative Models⁴⁷

On Measuring Social Biases in Prompt-Based Multi-Task Learning⁴⁸

Plot Writing From Pre-Trained Language Models⁴⁹

{S}tereo{S}et: Measuring stereotypical bias in pretrained language models⁵⁰

Survey of Hallucination in Natural Language Generation⁵¹

Wordcraft: Story Writing With Large Language Models⁵²

PainPoints: A Framework for Language-based Detection of Chronic Pain and Expert-Collaborative Text-Summarization⁵³

Self-Instruct: Aligning Language Model with Self Generated Instructions⁵⁴

From Images to Textual Prompts: Zero-shot VQA with Frozen Large Language Models⁵⁵

New and improved content moderation tooling⁵⁶

Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference⁵⁷

Human-level concept learning through probabilistic program induction⁵⁸

{Riffusion - Stable diffusion for real-time music generation}⁵⁹

How to use OpenAI’s ChatGPT to write the perfect cold email⁶⁰

Cacti: biology and uses⁶¹

Are Language Models Worse than Humans at Following Prompts? It’s Complicated⁶²

Unleashing Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration⁶³

Prompt Hacking

Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods⁶⁴

New jailbreak based on virtual functions - smuggle illegal tokens to the backend.⁶⁵

Exploiting Programmatic Behavior of LLMs: Dual-Use Through Standard Security Attacks⁶⁶

More than you've asked for: A Comprehensive Analysis of Novel Prompt Injection Threats to Application-Integrated Large Language Models⁶⁷

ChatGPT "DAN" (and other "Jailbreaks")⁶⁸

Evaluating the Susceptibility of Pre-Trained Language Models via Handcrafted Adversarial Examples⁶⁹

Prompt injection attacks against GPT-3⁷⁰

Exploiting GPT-3 prompts with malicious inputs that order the model to ignore its previous directions⁷¹

History Correction⁷²

adversarial-prompts⁷³

GPT-3 Prompt Injection Defenses⁷⁴

Talking to machines: prompt engineering & injection⁷⁵

Using GPT-Eliezer against ChatGPT Jailbreaking⁷⁶

Exploring Prompt Injection Attacks⁷⁷

The entire prompt of Microsoft Bing Chat?! (Hi, Sydney.)⁷⁸

Ignore Previous Prompt: Attack Techniques For Language Models⁷⁹

Lessons learned on Language Model Safety and misuse⁸⁰

Toxicity Detection with Generative Prompt-based Inference⁸¹

ok I saw a few people jailbreaking safeguards openai put on chatgpt so I had to give it a shot myself⁸²

Bypass @OpenAI's ChatGPT alignment efforts with this one weird trick⁸³

ChatGPT jailbreaking itself⁸⁴

Using "pretend" on #ChatGPT can do some wild stuff. You can kind of get some insight on the future, alternative universe.⁸⁵

I kinda like this one even more!⁸⁶

uh oh⁸⁷

Building A Virtual Machine inside ChatGPT⁸⁸

Reliability

MathPrompter: Mathematical Reasoning using Large Language Models⁸⁹

The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning⁹⁰

Prompting GPT-3 To Be Reliable⁹¹

On the Advance of Making Language Models Better Reasoners⁹²

Ask Me Anything: A simple strategy for prompting language models⁹³

Calibrate Before Use: Improving Few-Shot Performance of Language Models⁹⁴

Can large language models reason about medical questions?⁹⁵

Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inference⁹⁶

On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning⁹⁷

Evaluating language models can be tricky⁹⁸

Constitutional AI: Harmlessness from AI Feedback⁹⁹

Surveys

Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition¹⁰⁰

Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing¹⁰¹

PromptPapers¹⁰²

A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT¹⁰³

Techniques

Chain of Thought Prompting Elicits Reasoning in Large Language Models¹⁰⁴

Large Language Models are Zero-Shot Reasoners¹⁰⁵

Self-Consistency Improves Chain of Thought Reasoning in Language Models¹⁰⁶

What Makes Good In-Context Examples for GPT-3?¹⁰⁷

Generated Knowledge Prompting for Commonsense Reasoning¹⁰⁸

Recitation-Augmented Language Models¹⁰⁹

Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?¹¹⁰

Show Your Work: Scratchpads for Intermediate Computation with Language Models¹¹¹

Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations¹¹²

STaR: Bootstrapping Reasoning With Reasoning¹¹³

Least-to-Most Prompting Enables Complex Reasoning in Large Language Models¹¹⁴

Reframing Instructional Prompts to GPTk’s Language¹¹⁵

Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language Models¹¹⁶

Role-Play with Large Language Models¹¹⁷

CAMEL: Communicative Agents for "Mind" Exploration of Large Scale Language Model Society¹¹⁸

TELeR: A General Taxonomy of LLM Prompts for Benchmarking Complex Tasks¹¹⁹

Models

Image Models

Stable Diffusion¹²⁰

DALLE¹²¹

Language Models

ChatGPT¹²²

GPT-3¹²³

Instruct GPT¹²⁴

GPT-4¹²⁵

PaLM: Scaling Language Modeling with Pathways¹²⁶

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model¹²⁷

BLOOM+1: Adding Language Support to BLOOM for Zero-Shot Prompting¹²⁸

Jurassic-1: Technical Details and Evaluation, White paper, AI21 Labs, 2021¹²⁹

GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model¹³⁰

Roberta: A robustly optimized bert pretraining approach¹³¹

Tooling

Ides

TextBox 2.0: A Text Generation Library with Pre-trained Language Models¹³²

Interactive and Visual Prompt Engineering for Ad-hoc Task Adaptation with Large Language Models¹³³

PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts¹³⁴

PromptChainer: Chaining Large Language Model Prompts through Visual Programming¹³⁵

OpenPrompt: An Open-source Framework for Prompt-learning¹³⁶

PromptMaker: Prompt-Based Prototyping with Large Language Models¹³⁷

Tools

LangChain¹³⁸

GPT Index¹³⁹

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1. Karpas, E., Abend, O., Belinkov, Y., Lenz, B., Lieber, O., Ratner, N., Shoham, Y., Bata, H., Levine, Y., Leyton-Brown, K., Muhlgay, D., Rozen, N., Schwartz, E., Shachaf, G., Shalev-Shwartz, S., Shashua, A., & Tenenholtz, M. (2022). ↩
2. Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., & Cao, Y. (2022). ↩
3. Gao, L., Madaan, A., Zhou, S., Alon, U., Liu, P., Yang, Y., Callan, J., & Neubig, G. (2022). ↩
4. Significant-Gravitas. (2023). https://news.agpt.co/ ↩
5. Nakajima, Y. (2023). https://github.com/yoheinakajima/babyagi ↩
6. Reworkd.ai. (2023). https://github.com/reworkd/AgentGPT ↩
7. Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Zettlemoyer, L., Cancedda, N., & Scialom, T. (2023). ↩
8. Shin, T., Razeghi, Y., Logan IV, R. L., Wallace, E., & Singh, S. (2020). AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). https://doi.org/10.18653/v1/2020.emnlp-main.346 ↩
9. Zhou, Y., Muresanu, A. I., Han, Z., Paster, K., Pitis, S., Chan, H., & Ba, J. (2022). Large Language Models Are Human-Level Prompt Engineers. ↩
10. Lester, B., Al-Rfou, R., & Constant, N. (2021). The Power of Scale for Parameter-Efficient Prompt Tuning. ↩
11. Khashabi, D., Lyu, S., Min, S., Qin, L., Richardson, K., Welleck, S., Hajishirzi, H., Khot, T., Sabharwal, A., Singh, S., & Choi, Y. (2021). Prompt Waywardness: The Curious Case of Discretized Interpretation of Continuous Prompts. ↩
12. Lake, B. M., & Baroni, M. (2018). Generalization without Systematicity: On the Compositional Skills of Sequence-to-Sequence Recurrent Networks. https://doi.org/10.48550/arXiv.1711.00350 ↩
13. Cobbe, K., Kosaraju, V., Bavarian, M., Chen, M., Jun, H., Kaiser, L., Plappert, M., Tworek, J., Hilton, J., Nakano, R., Hesse, C., & Schulman, J. (2021). Training Verifiers to Solve Math Word Problems. ↩
14. Yang, Z., Qi, P., Zhang, S., Bengio, Y., Cohen, W. W., Salakhutdinov, R., & Manning, C. D. (2018). HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering. ↩
15. Roy, S., & Roth, D. (2015). Solving General Arithmetic Word Problems. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 1743–1752. https://doi.org/10.18653/v1/D15-1202 ↩
16. Thorne, J., Vlachos, A., Christodoulopoulos, C., & Mittal, A. (2018). FEVER: a large-scale dataset for Fact Extraction and VERification. ↩
17. Parrish, A., Chen, A., Nangia, N., Padmakumar, V., Phang, J., Thompson, J., Htut, P. M., & Bowman, S. R. (2021). BBQ: A Hand-Built Bias Benchmark for Question Answering. ↩
18. Roose, K. (2022). Don’t ban chatgpt in schools. teach with it. https://www.nytimes.com/2023/01/12/technology/chatgpt-schools-teachers.html ↩
19. Lipman, J., & Distler, R. (2023). Schools Shouldn’t Ban Access to ChatGPT. https://time.com/6246574/schools-shouldnt-ban-access-to-chatgpt/ ↩
20. Bansal, A., yeh Ping-Chiang, Curry, M., Jain, R., Wigington, C., Manjunatha, V., Dickerson, J. P., & Goldstein, T. (2022). Certified Neural Network Watermarks with Randomized Smoothing. ↩
21. Gu, C., Huang, C., Zheng, X., Chang, K.-W., & Hsieh, C.-J. (2022). Watermarking Pre-trained Language Models with Backdooring. ↩
22. Noonan, E., & Averill, O. (2023). GW preparing disciplinary response to AI programs as faculty explore educational use. https://www.gwhatchet.com/2023/01/17/gw-preparing-disciplinary-response-to-ai-programs-as-faculty-explore-educational-use/ ↩
23. Kirchenbauer, J., Geiping, J., Wen, Y., Katz, J., Miers, I., & Goldstein, T. (2023). A Watermark for Large Language Models. https://arxiv.org/abs/2301.10226 ↩
24. Mitchell, E., Lee, Y., Khazatsky, A., Manning, C., & Finn, C. (2023). DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature. https://doi.org/10.48550/arXiv.2301.11305 ↩
25. Oppenlaender, J. (2022). Prompt Engineering for Text-Based Generative Art. ↩
26. Parsons, G. (2022). The DALLE 2 Prompt Book. https://dallery.gallery/the-dalle-2-prompt-book/ ↩
27. Blake. (2022). With the right prompt, Stable Diffusion 2.0 can do hands. https://www.reddit.com/r/StableDiffusion/comments/z7salo/with_the_right_prompt_stable_diffusion_20_can_do/ ↩
28. Davenport, T. H., & Mittal, N. (2022). How Generative AI Is Changing Creative Work. Harvard Business Review. https://hbr.org/2022/11/how-generative-ai-is-changing-creative-work ↩
29. Captain, S. (2023). How AI Will Change the Workplace. Wall Street Journal. https://www.wsj.com/articles/how-ai-change-workplace-af2162ee ↩
30. Verma, P., & Vynck, G. D. (2023). ChatGPT took their jobs. Now they walk dogs and fix air conditioners. Washington Post. https://www.washingtonpost.com/technology/2023/06/02/ai-taking-jobs/ ↩
31. Ford, B. (2023). Bloomberg.Com. https://www.bloomberg.com/news/articles/2023-05-01/ibm-to-pause-hiring-for-back-office-jobs-that-ai-could-kill ↩
32. Efrat, A., & Levy, O. (2020). The Turking Test: Can Language Models Understand Instructions? ↩
33. Oppenlaender, J. (2022). A Taxonomy of Prompt Modifiers for Text-To-Image Generation. ↩
34. Wang, Z. J., Montoya, E., Munechika, D., Yang, H., Hoover, B., & Chau, D. H. (2022). DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Models. ↩
35. Hao, Y., Chi, Z., Dong, L., & Wei, F. (2022). Optimizing Prompts for Text-to-Image Generation. ↩
36. Dohan, D., Xu, W., Lewkowycz, A., Austin, J., Bieber, D., Lopes, R. G., Wu, Y., Michalewski, H., Saurous, R. A., Sohl-dickstein, J., Murphy, K., & Sutton, C. (2022). Language Model Cascades. ↩
37. Liu, V., & Chilton, L. B. (2022). Design Guidelines for Prompt Engineering Text-to-Image Generative Models. Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3491102.3501825 ↩
38. Perez, E., Ringer, S., Lukošiūtė, K., Nguyen, K., Chen, E., Heiner, S., Pettit, C., Olsson, C., Kundu, S., Kadavath, S., Jones, A., Chen, A., Mann, B., Israel, B., Seethor, B., McKinnon, C., Olah, C., Yan, D., Amodei, D., … Kaplan, J. (2022). Discovering Language Model Behaviors with Model-Written Evaluations. ↩
39. Su, H., Kasai, J., Wu, C. H., Shi, W., Wang, T., Xin, J., Zhang, R., Ostendorf, M., Zettlemoyer, L., Smith, N. A., & Yu, T. (2022). Selective Annotation Makes Language Models Better Few-Shot Learners. ↩
40. Izacard, G., Lewis, P., Lomeli, M., Hosseini, L., Petroni, F., Schick, T., Dwivedi-Yu, J., Joulin, A., Riedel, S., & Grave, E. (2022). Atlas: Few-shot Learning with Retrieval Augmented Language Models. ↩
41. Wang, B., Feng, C., Nair, A., Mao, M., Desai, J., Celikyilmaz, A., Li, H., Mehdad, Y., & Radev, D. (2022). STRUDEL: Structured Dialogue Summarization for Dialogue Comprehension. ↩
42. Beurer-Kellner, L., Fischer, M., & Vechev, M. (2022). Prompting Is Programming: A Query Language For Large Language Models. ↩
43. Ratner, N., Levine, Y., Belinkov, Y., Ram, O., Abend, O., Karpas, E., Shashua, A., Leyton-Brown, K., & Shoham, Y. (2022). Parallel Context Windows Improve In-Context Learning of Large Language Models. ↩
44. Bursztyn, V. S., Demeter, D., Downey, D., & Birnbaum, L. (2022). Learning to Perform Complex Tasks through Compositional Fine-Tuning of Language Models. ↩
45. Wang, Y., Mishra, S., Alipoormolabashi, P., Kordi, Y., Mirzaei, A., Arunkumar, A., Ashok, A., Dhanasekaran, A. S., Naik, A., Stap, D., Pathak, E., Karamanolakis, G., Lai, H. G., Purohit, I., Mondal, I., Anderson, J., Kuznia, K., Doshi, K., Patel, M., … Khashabi, D. (2022). Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks. ↩
46. Gao, T., Fisch, A., & Chen, D. (2021). Making Pre-trained Language Models Better Few-shot Learners. 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). https://doi.org/10.18653/v1/2021.acl-long.295 ↩
47. Dang, H., Mecke, L., Lehmann, F., Goller, S., & Buschek, D. (2022). How to Prompt? Opportunities and Challenges of Zero- and Few-Shot Learning for Human-AI Interaction in Creative Applications of Generative Models. ↩
48. Akyürek, A. F., Paik, S., Kocyigit, M. Y., Akbiyik, S., Runyun, Ş. L., & Wijaya, D. (2022). On Measuring Social Biases in Prompt-Based Multi-Task Learning. ↩
49. Jin, Y., Kadam, V., & Wanvarie, D. (2022). Plot Writing From Pre-Trained Language Models. ↩
50. Nadeem, M., Bethke, A., & Reddy, S. (2021). StereoSet: Measuring stereotypical bias in pretrained language models. 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), 5356–5371. https://doi.org/10.18653/v1/2021.acl-long.416 ↩
51. Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y., Madotto, A., & Fung, P. (2022). Survey of Hallucination in Natural Language Generation. ACM Computing Surveys. https://doi.org/10.1145/3571730 ↩
52. Yuan, A., Coenen, A., Reif, E., & Ippolito, D. (2022). Wordcraft: Story Writing With Large Language Models. 27th International Conference on Intelligent User Interfaces, 841–852. ↩
53. Fadnavis, S., Dhurandhar, A., Norel, R., Reinen, J. M., Agurto, C., Secchettin, E., Schweiger, V., Perini, G., & Cecchi, G. (2022). PainPoints: A Framework for Language-based Detection of Chronic Pain and Expert-Collaborative Text-Summarization. arXiv Preprint arXiv:2209.09814. ↩
54. Wang, Y., Kordi, Y., Mishra, S., Liu, A., Smith, N. A., Khashabi, D., & Hajishirzi, H. (2022). Self-Instruct: Aligning Language Model with Self Generated Instructions. ↩
55. Guo, J., Li, J., Li, D., Tiong, A. M. H., Li, B., Tao, D., & Hoi, S. C. H. (2022). From Images to Textual Prompts: Zero-shot VQA with Frozen Large Language Models. ↩
56. Markov, T. (2022). New and improved content moderation tooling. In OpenAI. OpenAI. https://openai.com/blog/new-and-improved-content-moderation-tooling/ ↩
57. Schick, T., & Schütze, H. (2020). Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference. ↩
58. Lake, B. M., Salakhutdinov, R., & Tenenbaum, J. B. (2015). Human-level concept learning through probabilistic program induction. Science, 350(6266), 1332–1338. ↩
59. Forsgren, S., & Martiros, H. (2022). Riffusion - Stable diffusion for real-time music generation. https://riffusion.com/about ↩
60. Bonta, A. (2022). How to use OpenAI’s ChatGPT to write the perfect cold email. https://www.streak.com/post/how-to-use-ai-to-write-perfect-cold-emails ↩
61. Nobel, P. S., & others. (2002). Cacti: biology and uses. Univ of California Press. ↩
62. Webson, A., Loo, A. M., Yu, Q., & Pavlick, E. (2023). Are Language Models Worse than Humans at Following Prompts? It’s Complicated. arXiv:2301.07085v1 [Cs.CL]. ↩
63. Wang, Z., Mao, S., Wu, W., Ge, T., Wei, F., & Ji, H. (2023). Unleashing Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration. ↩
64. Crothers, E., Japkowicz, N., & Viktor, H. (2022). Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods. ↩
65. u/Nin_kat. (2023). New jailbreak based on virtual functions - smuggle illegal tokens to the backend. https://www.reddit.com/r/ChatGPT/comments/10urbdj/new_jailbreak_based_on_virtual_functions_smuggle ↩
66. Kang, D., Li, X., Stoica, I., Guestrin, C., Zaharia, M., & Hashimoto, T. (2023). Exploiting Programmatic Behavior of LLMs: Dual-Use Through Standard Security Attacks. ↩
67. Greshake, K., Abdelnabi, S., Mishra, S., Endres, C., Holz, T., & Fritz, M. (2023). More than you’ve asked for: A Comprehensive Analysis of Novel Prompt Injection Threats to Application-Integrated Large Language Models. ↩
68. KIHO, L. (2023). ChatGPT “DAN” (and other “Jailbreaks”). https://github.com/0xk1h0/ChatGPT_DAN ↩
69. Branch, H. J., Cefalu, J. R., McHugh, J., Hujer, L., Bahl, A., del Castillo Iglesias, D., Heichman, R., & Darwishi, R. (2022). Evaluating the Susceptibility of Pre-Trained Language Models via Handcrafted Adversarial Examples. ↩
70. Willison, S. (2022). Prompt injection attacks against GPT-3. https://simonwillison.net/2022/Sep/12/prompt-injection/ ↩
71. Goodside, R. (2022). Exploiting GPT-3 prompts with malicious inputs that order the model to ignore its previous directions. https://twitter.com/goodside/status/1569128808308957185 ↩
72. Goodside, R. (2023). History Correction. https://twitter.com/goodside/status/1610110111791325188?s=20&t=ulviQABPXFIIt4ZNZPAUCQ ↩
73. Chase, H. (2022). adversarial-prompts. https://github.com/hwchase17/adversarial-prompts ↩
74. Goodside, R. (2022). GPT-3 Prompt Injection Defenses. https://twitter.com/goodside/status/1578278974526222336?s=20&t=3UMZB7ntYhwAk3QLpKMAbw ↩
75. Mark, C. (2022). Talking to machines: prompt engineering & injection. https://artifact-research.com/artificial-intelligence/talking-to-machines-prompt-engineering-injection/ ↩
76. Stuart Armstrong, R. G. (2022). Using GPT-Eliezer against ChatGPT Jailbreaking. https://www.alignmentforum.org/posts/pNcFYZnPdXyL2RfgA/using-gpt-eliezer-against-chatgpt-jailbreaking ↩
77. Selvi, J. (2022). Exploring Prompt Injection Attacks. https://research.nccgroup.com/2022/12/05/exploring-prompt-injection-attacks/ ↩
78. Liu, K. (2023). The entire prompt of Microsoft Bing Chat?! (Hi, Sydney.). https://twitter.com/kliu128/status/1623472922374574080 ↩
79. Perez, F., & Ribeiro, I. (2022). Ignore Previous Prompt: Attack Techniques For Language Models. arXiv. https://doi.org/10.48550/ARXIV.2211.09527 ↩
80. Brundage, M. (2022). Lessons learned on Language Model Safety and misuse. In OpenAI. OpenAI. https://openai.com/blog/language-model-safety-and-misuse/ ↩
81. Wang, Y.-S., & Chang, Y. (2022). Toxicity Detection with Generative Prompt-based Inference. arXiv. https://doi.org/10.48550/ARXIV.2205.12390 ↩
82. Maz, A. (2022). ok I saw a few people jailbreaking safeguards openai put on chatgpt so I had to give it a shot myself. https://twitter.com/alicemazzy/status/1598288519301976064 ↩
83. Piedrafita, M. (2022). Bypass @OpenAI’s ChatGPT alignment efforts with this one weird trick. https://twitter.com/m1guelpf/status/1598203861294252033 ↩
84. Parfait, D. (2022). ChatGPT jailbreaking itself. https://twitter.com/haus_cole/status/1598541468058390534 ↩
85. Soares, N. (2022). Using “pretend” on #ChatGPT can do some wild stuff. You can kind of get some insight on the future, alternative universe. https://twitter.com/NeroSoares/status/1608527467265904643 ↩
86. Moran, N. (2022). I kinda like this one even more! https://twitter.com/NickEMoran/status/1598101579626057728 ↩
87. samczsun. (2022). uh oh. https://twitter.com/samczsun/status/1598679658488217601 ↩
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