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📚 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​

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​


  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 ↩
  88. Degrave, J. (2022). Building A Virtual Machine inside ChatGPT. Engraved. https://www.engraved.blog/building-a-virtual-machine-inside/ ↩
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