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Last updated on August 7, 2024

سینڈر شلہوف

صفحہ اس کورس کے ذریعہ استعمال ہونے والے تمام کاغذات کی ایک منظم فہرست پر مشتمل ہے۔ مقالے عنوان کے لحاظ سے ترتیب دیئے گئے ہیں۔

اس کورس کا حوالہ دینے کے لیے، Github ذخیرہ میں فراہم کردہ حوالہ استعمال کریں۔

@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}
}

نوٹ: چونکہ نہ تو GPT-3 اور نہ ہی GPT-3 انسٹرکٹ پیپر ڈیونچی ماڈلز سے مطابقت رکھتا ہے، میں کوشش کرتا ہوں کہ اس طرح ان کا حوالہ دیتے ہیں.

AUTOGENERATED BELOW, DO NOT EDIT

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

Footnotes

  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. arXiv Preprint arXiv:2010.15980. ↩

  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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