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Daftar Pustaka

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Reading Time: 84 minutes

Last updated on August 7, 2024

Halaman ini berisi daftar terorganisir dari semua makalah yang digunakan oleh kursus ini. Makalah-makalah tersebut diatur berdasarkan topik.

Untuk mengutip kursus ini, gunakan kutipan yang disediakan di repositori Github.

@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}
}

Catatan: karena baik GPT-3 maupun GPT-3 Instruct paper tidak sesuai dengan model davinci, saya berusaha untuk tidak mengutipnya sebagai model tersebut.

AUTOGENERATED BELOW, DO NOT EDIT

Agen

MRKL

ReAct

PAL

Auto-GPT

Baby AGI

AgentGPT

Toolformer

Otomatisasi

AutoPrompt: Mengumpulkan Pengetahuan dari Model Bahasa dengan Prompts yang Dibuat Secara Otomatis

automatic prompt engineer

Soft Prompting

discretized soft prompting (interpreting)

Dataset

SCAN dataset (compositional generalization)

GSM8K

hotpotQA

multiarith

fever dataset

bbq

Pendeteksi

Jangan melarang chatgpt di sekolah. mengajar dengan chatgpt.

Sekolah-sekolah Sebaiknya Tidak Melarang Akses ke ChatGPT

Certified Neural Network Watermarks with Randomized Smoothing

Watermarking Pre-trained Language Models dengan Backdooring

GW menyiapkan respons disiplin terhadap program AI saat fakultas menjelajahi penggunaan pendidikan

A Watermark for Large Language Models

DetectGPT: Deteksi Teks yang Dibuat oleh Mesin 'Zero-Shot' dengan Menggunakan Probabilitas Kurva

Prompt Engineering untuk Gambar

Prompt Engineering for Text-Based Generative Art

The DALLE 2 Prompt Book

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

Serba Aneka

The Turking Test: Can Language Models Understand Instructions?

Taksonomi Pengubah Prompt untuk Menghasilkan Text-To-Image

DiffusionDB: Dataset Galeri Prompt Skala Besar untuk Model Generatif Text-To-Image

Optimizing Prompts for Text-to-Image Generation

Language Model Cascades

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

Discovering Language Model Behaviors with Model-Written Evaluations

Selective Annotation Makes Language Models Better Few-Shot Learners

Atlas: Few-shot Learning with Retrieval Augmented Language Models

STRUDEL: Structured Dialogue Summarization for Dialogue Comprehension

Prompting Is Programming: A Query Language For Large Language Models

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

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

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

Making Pre-trained Language Models Better Few-shot Learners

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

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

Plot Writing From Pre-Trained Language Models

{S}tereo{S}et: Mengukur bias stereotip dalam model bahasa terlatih sebelumnya

Survey of Hallucination in Natural Language Generation

Wordcraft: Menulis Cerita dengan Model Bahasa Besar

PainPoints: Sebuah Kerangka Kerja untuk Deteksi Nyeri Kronis berbasis Bahasa dan Ringkasan Teks Kolaboratif Ahli

Self-Instruct: Aligning Language Model with Self Generated Instructions

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

New and improved content moderation tooling

No title

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

Pembelajaran konsep level manusia melalui induksi program probabilistik

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

Cara menggunakan ChatGPT dari OpenAI untuk menulis cold email yang sempurna

Cacti: biology and uses

Apakah Model Bahasa Lebih Buruk daripada Manusia dalam Mengikuti Petunjuk? Ini Rumit

Mengungkap Kebersamaan Kognitif dalam Model Bahasa Besar: Agen Penyelesaian Tugas melalui Kolaborasi Diri Multi-Persona

Prompt Hacking

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

Jebol baru berdasarkan fungsi virtual - menyelundupkan token ilegal ke backend.

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

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

ChatGPT "DAN" (and other "Jailbreaks")

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

Prompt injection attacks against GPT-3

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

History Correction

adversarial-prompts

GPT-3 Prompt Injection Defenses

Talking to machines: prompt engineering & injection

Using GPT-Eliezer against ChatGPT Jailbreaking

Exploring Prompt Injection Attacks

Seluruh permintaan Bing Chat Microsoft?! (Halo, Sydney.)

Ignore Previous Prompt: Attack Techniques For Language Models

Lessons learned on Language Model Safety and misuse

Toxicity Detection with Generative Prompt-based Inference

ok saya melihat beberapa orang membobol perlindungan yang diberikan oleh openai pada chatgpt, jadi saya harus mencobanya sendiri

Melewati upaya penyelarasan ChatGPT @OpenAI dengan trik aneh ini

ChatGPT membobol dirinya sendiri

Menggunakan "pretend" di #ChatGPT bisa melakukan beberapa hal yang luar biasa. Anda dapat sedikit mendapatkan wawasan tentang masa depan, alam semesta alternatif.

Aku agak lebih suka yang ini, bahkan lebih!

uh oh

Membangun Mesin Virtual di dalam ChatGPT

Keandalan

MathPrompter: Reasoning Matematika menggunakan Model Bahasa Besar

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

Prompting GPT-3 To Be Reliable

Pada Kemajuan dalam Meningkatkan Model Bahasa Sebagai Pemikir yang Lebih Baik

Tanyakan Apa Saja pada Saya: Sebuah strategi sederhana untuk memicu model bahasa

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

Apakah model bahasa besar dapat melakukan penalaran tentang pertanyaan medis?

Meningkatkan Konsistensi Diri dan Performa dari Model Bahasa Pra-terlatih melalui Inferensi Bahasa Alami

Kalau Dipikir-pikir Lagi, Mari Kita Tidak Berpikir Langkah demi Langkah! Bias dan Toxicity pda Zero-Shot Reasoning

Mengevaluasi model bahasa bisa saja sulit

Survey

Speech and Language Processing: Pengantar Pemrosesan Bahasa Alami, Linguistik Komputasional, dan Pengenalan Suara

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

PromptPapers

A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT

Teknik

Chain of Thought Prompting Penalaran dalam Model Bahasa Besar

Large Language Model adalah Zero-Shot Reasoners

Ketetapan Diri Meningkatkan Rantai Berpikir Penalaran pada Model Bahasa

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

Prompt Pengetahuan yang Dihasilkan untuk Penalaran Wajar

Recitation-Augmented Language Models

Mempertimbangkan Kembali Peran Demonstrasi: Apa yang Membuat Pembelajaran Kontekstual Bekerja?

Tunjukkan Pekerjaan Anda: Scratchpads untuk Komputasi Menengah dengan Model Bahasa

Maieutic Prompting: Penalaran yang Logis dan Konsisten dengan Penjelasan Rekursif

STaR: Memulai Penalaran Dengan Penalaran

Prompt Least-to-Most Memungkinkan Pemikiran Kompleks dalam Model Bahasa Besar

Reframing Instructional Prompts to GPTk’s Language

Memangkas Prompt dan Parameter: Pembelajaran Few-Shot Sederhana dengan Model Bahasa

Role-Play dengan Model Bahasa Besar

CAMEL: Agen Komunikatif untuk "Eksplorasi" Pikiran Masyarakat Model Bahasa Skala Besar

TELeR: Taksonomi Umum dari LLM Prompts untuk Benchmarking Tugas Kompleks

Model

Model Gambar

Stable Diffusion

DALLE

Model Bahasa

ChatGPT

GPT-3

Instruct GPT

GPT-4

PaLM: Memperbesar Pembentukan Bahasa dengan Pathways

BLOOM: Sebuah Model Bahasa Multilingual Open-Access dengan 176B Parameter

BLOOM+1: Menambahkan Dukungan Bahasa ke BLOOM untuk Prompt Zero-Shot

Jurassic-1: Detail Teknis dan Evaluasi, White paper, AI21 Labs, 2021

GPT-J-6B: Sebuah Model Bahasa Autoregresif dengan 6 Miliar Parameter

Roberta: Pendekatan pra-pelatihan bert yang dioptimalkan secara kuat

Tooling

Ides

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

Prompt Engineering Interaktif dan Visual untuk Adaptasi Tugas Ad-hoc dengan Model Bahasa Besar

PromptSource: Lingkungan Pengembangan Terpadu dan Repositori untuk Promp Bahasa Alami

PromptChainer: Menghubungkan Prompt Model Bahasa yang Besar melalui Pemrograman Visual

OpenPrompt: An Open-source Framework for Prompt-learning

PromptMaker: Prompt-Based Prototyping dengan Large Language Models

Tools

LangChain

GPT Index

Sander Schulhoff

Sander Schulhoff is the Founder of Learn Prompting and an ML Researcher at the University of Maryland. He created the first open-source Prompt Engineering guide, reaching 3M+ people and teaching them to use tools like ChatGPT. Sander also led a team behind Prompt Report, the most comprehensive study of prompting ever done, co-authored with researchers from the University of Maryland, OpenAI, Microsoft, Google, Princeton, Stanford, and other leading institutions. This 76-page survey analyzed 1,500+ academic papers and covered 200+ prompting techniques.

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. Efrat, A., & Levy, O. (2020). The Turking Test: Can Language Models Understand Instructions? ↩

  29. Oppenlaender, J. (2022). A Taxonomy of Prompt Modifiers for Text-To-Image Generation. ↩

  30. 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. ↩

  31. Hao, Y., Chi, Z., Dong, L., & Wei, F. (2022). Optimizing Prompts for Text-to-Image Generation. ↩

  32. 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. ↩

  33. 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 ↩

  34. 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. ↩

  35. 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. ↩

  36. 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. ↩

  37. 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. ↩

  38. Beurer-Kellner, L., Fischer, M., & Vechev, M. (2022). Prompting Is Programming: A Query Language For Large Language Models. ↩

  39. 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. ↩

  40. Bursztyn, V. S., Demeter, D., Downey, D., & Birnbaum, L. (2022). Learning to Perform Complex Tasks through Compositional Fine-Tuning of Language Models. ↩

  41. 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. ↩

  42. 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 ↩

  43. 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. ↩

  44. 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. ↩

  45. Jin, Y., Kadam, V., & Wanvarie, D. (2022). Plot Writing From Pre-Trained Language Models. ↩

  46. 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 ↩

  47. 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 ↩

  48. 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. ↩

  49. 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. ↩

  50. 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. ↩

  51. 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. ↩

  52. Markov, T. (2022). New and improved content moderation tooling. In OpenAI. OpenAI. https://openai.com/blog/new-and-improved-content-moderation-tooling/ ↩

  53. OpenAI. (2022). https://beta.openai.com/docs/guides/moderation ↩

  54. Schick, T., & Schütze, H. (2020). Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference. ↩

  55. Lake, B. M., Salakhutdinov, R., & Tenenbaum, J. B. (2015). Human-level concept learning through probabilistic program induction. Science, 350(6266), 1332–1338. ↩

  56. Forsgren, S., & Martiros, H. (2022). Riffusion - Stable diffusion for real-time music generation. https://riffusion.com/about ↩

  57. 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 ↩

  58. Nobel, P. S., & others. (2002). Cacti: biology and uses. Univ of California Press. ↩

  59. 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]. ↩

  60. 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. ↩

  61. Crothers, E., Japkowicz, N., & Viktor, H. (2022). Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods. ↩

  62. 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 ↩

  63. Kang, D., Li, X., Stoica, I., Guestrin, C., Zaharia, M., & Hashimoto, T. (2023). Exploiting Programmatic Behavior of LLMs: Dual-Use Through Standard Security Attacks. ↩

  64. 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. ↩

  65. KIHO, L. (2023). ChatGPT “DAN” (and other “Jailbreaks”). https://github.com/0xk1h0/ChatGPT_DAN ↩

  66. 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. ↩

  67. Willison, S. (2022). Prompt injection attacks against GPT-3. https://simonwillison.net/2022/Sep/12/prompt-injection/ ↩

  68. 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 ↩

  69. Goodside, R. (2023). History Correction. https://twitter.com/goodside/status/1610110111791325188?s=20&t=ulviQABPXFIIt4ZNZPAUCQ ↩

  70. Chase, H. (2022). adversarial-prompts. https://github.com/hwchase17/adversarial-prompts ↩

  71. Goodside, R. (2022). GPT-3 Prompt Injection Defenses. https://twitter.com/goodside/status/1578278974526222336?s=20&t=3UMZB7ntYhwAk3QLpKMAbw ↩

  72. Mark, C. (2022). Talking to machines: prompt engineering & injection. https://artifact-research.com/artificial-intelligence/talking-to-machines-prompt-engineering-injection/ ↩

  73. Stuart Armstrong, R. G. (2022). Using GPT-Eliezer against ChatGPT Jailbreaking. https://www.alignmentforum.org/posts/pNcFYZnPdXyL2RfgA/using-gpt-eliezer-against-chatgpt-jailbreaking ↩

  74. Selvi, J. (2022). Exploring Prompt Injection Attacks. https://research.nccgroup.com/2022/12/05/exploring-prompt-injection-attacks/ ↩

  75. Liu, K. (2023). The entire prompt of Microsoft Bing Chat?! (Hi, Sydney.). https://twitter.com/kliu128/status/1623472922374574080 ↩

  76. Perez, F., & Ribeiro, I. (2022). Ignore Previous Prompt: Attack Techniques For Language Models. arXiv. https://doi.org/10.48550/ARXIV.2211.09527 ↩

  77. Brundage, M. (2022). Lessons learned on Language Model Safety and misuse. In OpenAI. OpenAI. https://openai.com/blog/language-model-safety-and-misuse/ ↩

  78. Wang, Y.-S., & Chang, Y. (2022). Toxicity Detection with Generative Prompt-based Inference. arXiv. https://doi.org/10.48550/ARXIV.2205.12390 ↩

  79. 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 ↩

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