📚 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.

🔵 = Paper directly cited in this course. Other papers have informed my understanding of the topic.

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

Prompt Engineering Strategies

Chain of Thought¹ 🔵

Zero Shot Chain of Thought² 🔵

Self Consistency³ 🔵

What Makes Good In-Context Examples for GPT-3?⁴ 🔵

Ask-Me-Anything Prompting⁵ 🔵

Generated Knowledge⁶ 🔵

Recitation-Augmented Language Models⁷ 🔵

Rethinking the role of demonstrations⁸ 🔵

Scratchpads⁹

Maieutic Prompting¹⁰

STaR¹¹

Least to Most¹² 🔵

Reframing Instructional Prompts to GPTk’s Language¹³ 🔵

The Turking Test: Can Language Models Understand Instructions?¹⁴ 🔵

Reliability

MathPrompter¹⁵ 🔵

The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning¹⁶ 🔵

Prompting GPT-3 to be reliable¹⁷

Diverse Prompts¹⁸ 🔵

Calibrate Before Use: Improving Few-Shot Performance of Language Models¹⁹ 🔵

Enhanced Self Consistency²⁰

Bias and Toxicity in Zero-Shot CoT²¹ 🔵

Constitutional AI: Harmlessness from AI Feedback²² 🔵

Compositional Generalization - SCAN²³

Automated Prompt Engineering

AutoPrompt²⁴ 🔵

Automatic Prompt Engineer²⁵

Models

Language Models

GPT-3²⁶ 🔵

GPT-3 Instruct²⁷ 🔵

PaLM²⁸ 🔵

BLOOM²⁹ 🔵

BLOOM+1 (more languages/ 0 shot improvements)³⁰

Jurassic 1³¹ 🔵

GPT-J-6B³²

Roberta³³

Image Models

Stable Diffusion³⁴ 🔵

DALLE³⁵ 🔵

Soft Prompting

Soft Prompting³⁶ 🔵

Interpretable Discretized Soft Prompts³⁷ 🔵

Datasets

MultiArith³⁸ 🔵

GSM8K³⁹ 🔵

HotPotQA⁴⁰ 🔵

Fever⁴¹ 🔵

BBQ: A Hand-Built Bias Benchmark for Question Answering⁴² 🔵

Image Prompt Engineering

Taxonomy of prompt modifiers⁴³

DiffusionDB⁴⁴

The DALLE 2 Prompt Book⁴⁵ 🔵

Prompt Engineering for Text-Based Generative Art⁴⁶ 🔵

With the right prompt, Stable Diffusion 2.0 can do hands.⁴⁷ 🔵

Optimizing Prompts for Text-to-Image Generation⁴⁸

Prompt Engineering IDEs

Prompt IDE⁴⁹ 🔵

Prompt Source⁵⁰ 🔵

PromptChainer⁵¹ 🔵

PromptMaker⁵² 🔵

Tooling

LangChain⁵³ 🔵

TextBox 2.0: A Text Generation Library with Pre-trained Language Models⁵⁴ 🔵

OpenPrompt: An Open-source Framework for Prompt-learning⁵⁵ 🔵

GPT Index⁵⁶ 🔵

Applied Prompt Engineering

Language Model Cascades⁵⁷

MRKL⁵⁸ 🔵

ReAct⁵⁹ 🔵

PAL: Program-aided Language Models⁶⁰ 🔵

User Interface Design

Design Guidelines for Prompt Engineering Text-to-Image Generative Models⁶¹

Prompt Injection

Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods⁶² 🔵

Evaluating the Susceptibility of Pre-Trained Language Models via Handcrafted Adversarial Examples⁶³ 🔵

Prompt injection attacks against GPT-3⁶⁴ 🔵

Exploiting GPT-3 prompts with malicious inputs that order the model to ignore its previous directions⁶⁵ 🔵

adversarial-prompts⁶⁶ 🔵

GPT-3 Prompt Injection Defenses⁶⁷ 🔵

Talking to machines: prompt engineering & injection⁶⁸

Exploring Prompt Injection Attacks⁶⁹ 🔵

Using GPT-Eliezer against ChatGPT Jailbreaking⁷⁰ 🔵

Microsoft Bing Chat Prompt⁷¹

Jailbreaking

Ignore Previous Prompt: Attack Techniques For Language Models⁷²

Lessons learned on Language Model Safety and misuse⁷³

Toxicity Detection with Generative Prompt-based Inference⁷⁴

New and improved content moderation tooling⁷⁵

OpenAI API⁷⁶ 🔵

OpenAI ChatGPT⁷⁷ 🔵

ChatGPT 4 Tweet⁷⁸ 🔵

Acting Tweet⁷⁹ 🔵

Research Tweet⁸⁰ 🔵

Pretend Ability Tweet⁸¹ 🔵

Responsibility Tweet⁸² 🔵

Lynx Mode Tweet⁸³ 🔵

Sudo Mode Tweet⁸⁴ 🔵

Ignore Previous Prompt⁸⁵ 🔵

Updated Jailbreaking Prompts⁸⁶ 🔵

Surveys

Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing⁸⁷

PromptPapers⁸⁸

Dataset Generation

Discovering Language Model Behaviors with Model-Written Evaluations⁸⁹

Selective Annotation Makes Language Models Better Few-Shot Learners⁹⁰

Applications

Atlas: Few-shot Learning with Retrieval Augmented Language Models⁹¹

STRUDEL: Structured Dialogue Summarization for Dialogue Comprehension⁹²

Miscl

Prompting Is Programming: A Query Language For Large Language Models⁹³

Parallel Context Windows Improve In-Context Learning of Large Language Models⁹⁴

A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT⁹⁵ 🔵

Learning to Perform Complex Tasks through Compositional Fine-Tuning of Language Models⁹⁶

Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks⁹⁷

Making Pre-trained Language Models Better Few-shot Learners⁹⁸

Grounding with search results⁹⁹

How to Prompt? Opportunities and Challenges of Zero- and Few-Shot Learning for Human-AI Interaction in Creative Applications of Generative Models¹⁰⁰

On Measuring Social Biases in Prompt-Based Multi-Task Learning¹⁰¹

Plot Writing From Pre-Trained Language Models¹⁰² 🔵

StereoSet: Measuring stereotypical bias in pretrained language models¹⁰³

Survey of Hallucination in Natural Language Generation¹⁰⁴

Examples¹⁰⁵

Wordcraft¹⁰⁶

PainPoints¹⁰⁷

Self-Instruct: Aligning Language Model with Self Generated Instructions¹⁰⁸

From Images to Textual Prompts: Zero-shot VQA with Frozen Large Language Models¹⁰⁹

Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference¹¹⁰

Ask-Me-Anything Prompting⁵

A Watermark for Large Language Models¹¹¹

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1. Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., & Zhou, D. (2022). Chain of Thought Prompting Elicits Reasoning in Large Language Models. ↩
2. Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., & Iwasawa, Y. (2022). Large Language Models are Zero-Shot Reasoners. ↩
3. Wang, X., Wei, J., Schuurmans, D., Le, Q., Chi, E., Narang, S., Chowdhery, A., & Zhou, D. (2022). Self-Consistency Improves Chain of Thought Reasoning in Language Models. ↩
4. Liu, J., Shen, D., Zhang, Y., Dolan, B., Carin, L., & Chen, W. (2021). What Makes Good In-Context Examples for GPT-3? ↩
5. Arora, S., Narayan, A., Chen, M. F., Orr, L., Guha, N., Bhatia, K., Chami, I., Sala, F., & Ré, C. (2022). Ask Me Anything: A simple strategy for prompting language models. ↩
6. Liu, J., Liu, A., Lu, X., Welleck, S., West, P., Bras, R. L., Choi, Y., & Hajishirzi, H. (2021). Generated Knowledge Prompting for Commonsense Reasoning. ↩
7. Sun, Z., Wang, X., Tay, Y., Yang, Y., & Zhou, D. (2022). Recitation-Augmented Language Models. ↩
8. Min, S., Lyu, X., Holtzman, A., Artetxe, M., Lewis, M., Hajishirzi, H., & Zettlemoyer, L. (2022). Rethinking the Role of Demonstrations: What Makes In-Context Learning Work? ↩
9. Nye, M., Andreassen, A. J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., & Odena, A. (2021). Show Your Work: Scratchpads for Intermediate Computation with Language Models. ↩
10. Jung, J., Qin, L., Welleck, S., Brahman, F., Bhagavatula, C., Bras, R. L., & Choi, Y. (2022). Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations. ↩
11. Zelikman, E., Wu, Y., Mu, J., & Goodman, N. D. (2022). STaR: Bootstrapping Reasoning With Reasoning. ↩
12. Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., & Chi, E. (2022). Least-to-Most Prompting Enables Complex Reasoning in Large Language Models. ↩
13. Mishra, S., Khashabi, D., Baral, C., Choi, Y., & Hajishirzi, H. (2022). Reframing Instructional Prompts to GPTk’s Language. Findings of the Association for Computational Linguistics: ACL 2022. https://doi.org/10.18653/v1/2022.findings-acl.50 ↩
14. Efrat, A., & Levy, O. (2020). The Turking Test: Can Language Models Understand Instructions? ↩
15. Imani, S., Du, L., & Shrivastava, H. (2023). MathPrompter: Mathematical Reasoning using Large Language Models. ↩
16. Ye, X., & Durrett, G. (2022). The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning. ↩
17. Si, C., Gan, Z., Yang, Z., Wang, S., Wang, J., Boyd-Graber, J., & Wang, L. (2022). Prompting GPT-3 To Be Reliable. ↩
18. Li, Y., Lin, Z., Zhang, S., Fu, Q., Chen, B., Lou, J.-G., & Chen, W. (2022). On the Advance of Making Language Models Better Reasoners. ↩
19. Zhao, T. Z., Wallace, E., Feng, S., Klein, D., & Singh, S. (2021). Calibrate Before Use: Improving Few-Shot Performance of Language Models. ↩
20. Mitchell, E., Noh, J. J., Li, S., Armstrong, W. S., Agarwal, A., Liu, P., Finn, C., & Manning, C. D. (2022). Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inference. ↩
21. Shaikh, O., Zhang, H., Held, W., Bernstein, M., & Yang, D. (2022). On Second Thought, Let’s Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning. ↩
22. Bai, Y., Kadavath, S., Kundu, S., Askell, A., Kernion, J., Jones, A., Chen, A., Goldie, A., Mirhoseini, A., McKinnon, C., Chen, C., Olsson, C., Olah, C., Hernandez, D., Drain, D., Ganguli, D., Li, D., Tran-Johnson, E., Perez, E., … Kaplan, J. (2022). Constitutional AI: Harmlessness from AI Feedback. ↩
23. 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 ↩
24. 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 ↩
25. Zhou, Y., Muresanu, A. I., Han, Z., Paster, K., Pitis, S., Chan, H., & Ba, J. (2022). Large Language Models Are Human-Level Prompt Engineers. ↩
26. Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., … Amodei, D. (2020). Language Models are Few-Shot Learners. ↩
27. Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P., Leike, J., & Lowe, R. (2022). Training language models to follow instructions with human feedback. ↩
28. Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., Schuh, P., Shi, K., Tsvyashchenko, S., Maynez, J., Rao, A., Barnes, P., Tay, Y., Shazeer, N., Prabhakaran, V., … Fiedel, N. (2022). PaLM: Scaling Language Modeling with Pathways. ↩
29. Scao, T. L., Fan, A., Akiki, C., Pavlick, E., Ilić, S., Hesslow, D., Castagné, R., Luccioni, A. S., Yvon, F., Gallé, M., Tow, J., Rush, A. M., Biderman, S., Webson, A., Ammanamanchi, P. S., Wang, T., Sagot, B., Muennighoff, N., del Moral, A. V., … Wolf, T. (2022). BLOOM: A 176B-Parameter Open-Access Multilingual Language Model. ↩
30. Yong, Z.-X., Schoelkopf, H., Muennighoff, N., Aji, A. F., Adelani, D. I., Almubarak, K., Bari, M. S., Sutawika, L., Kasai, J., Baruwa, A., Winata, G. I., Biderman, S., Radev, D., & Nikoulina, V. (2022). BLOOM+1: Adding Language Support to BLOOM for Zero-Shot Prompting. ↩
31. Lieber, O., Sharir, O., Lentz, B., & Shoham, Y. (2021). Jurassic-1: Technical Details and Evaluation, White paper, AI21 Labs, 2021. URL: Https://Uploads-Ssl. Webflow. Com/60fd4503684b466578c0d307/61138924626a6981ee09caf6_jurassic_ Tech_paper. Pdf. ↩
32. Wang, B., & Komatsuzaki, A. (2021). GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model. https://github.com/kingoflolz/mesh-transformer-jax. https://github.com/kingoflolz/mesh-transformer-jax ↩
33. Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). Roberta: A robustly optimized bert pretraining approach. arXiv Preprint arXiv:1907.11692. ↩
34. Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2021). High-Resolution Image Synthesis with Latent Diffusion Models. ↩
35. Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., & Chen, M. (2022). Hierarchical Text-Conditional Image Generation with CLIP Latents. ↩
36. Lester, B., Al-Rfou, R., & Constant, N. (2021). The Power of Scale for Parameter-Efficient Prompt Tuning. ↩
37. 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. ↩
38. 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 ↩
39. 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. ↩
40. 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. ↩
41. Thorne, J., Vlachos, A., Christodoulopoulos, C., & Mittal, A. (2018). FEVER: a large-scale dataset for Fact Extraction and VERification. ↩
42. 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. ↩
43. Oppenlaender, J. (2022). A Taxonomy of Prompt Modifiers for Text-To-Image Generation. ↩
44. 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. ↩
45. Parsons, G. (2022). The DALLE 2 Prompt Book. https://dallery.gallery/the-dalle-2-prompt-book/ ↩
46. Oppenlaender, J. (2022). Prompt Engineering for Text-Based Generative Art. ↩
47. 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/ ↩
48. Hao, Y., Chi, Z., Dong, L., & Wei, F. (2022). Optimizing Prompts for Text-to-Image Generation. ↩
49. Strobelt, H., Webson, A., Sanh, V., Hoover, B., Beyer, J., Pfister, H., & Rush, A. M. (2022). Interactive and Visual Prompt Engineering for Ad-hoc Task Adaptation with Large Language Models. arXiv. https://doi.org/10.48550/ARXIV.2208.07852 ↩
50. Bach, S. H., Sanh, V., Yong, Z.-X., Webson, A., Raffel, C., Nayak, N. V., Sharma, A., Kim, T., Bari, M. S., Fevry, T., Alyafeai, Z., Dey, M., Santilli, A., Sun, Z., Ben-David, S., Xu, C., Chhablani, G., Wang, H., Fries, J. A., … Rush, A. M. (2022). PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts. ↩
51. Wu, T., Jiang, E., Donsbach, A., Gray, J., Molina, A., Terry, M., & Cai, C. J. (2022). PromptChainer: Chaining Large Language Model Prompts through Visual Programming. ↩
52. Jiang, E., Olson, K., Toh, E., Molina, A., Donsbach, A., Terry, M., & Cai, C. J. (2022). PromptMaker: Prompt-Based Prototyping with Large Language Models. Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3491101.3503564 ↩
53. Chase, H. (2022). LangChain (0.0.66) [Computer software]. https://github.com/hwchase17/langchain ↩
54. Tang, T., Junyi, L., Chen, Z., Hu, Y., Yu, Z., Dai, W., Dong, Z., Cheng, X., Wang, Y., Zhao, W., Nie, J., & Wen, J.-R. (2022). TextBox 2.0: A Text Generation Library with Pre-trained Language Models. ↩
55. Ding, N., Hu, S., Zhao, W., Chen, Y., Liu, Z., Zheng, H.-T., & Sun, M. (2021). OpenPrompt: An Open-source Framework for Prompt-learning. arXiv Preprint arXiv:2111.01998. ↩
56. Liu, J. (2022). GPT Index. https://doi.org/10.5281/zenodo.1234 ↩
57. 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. ↩
58. 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). MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning. ↩
59. Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., & Cao, Y. (2022). ReAct: Synergizing Reasoning and Acting in Language Models. ↩
60. Gao, L., Madaan, A., Zhou, S., Alon, U., Liu, P., Yang, Y., Callan, J., & Neubig, G. (2022). PAL: Program-aided Language Models. ↩
61. 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 ↩
62. Crothers, E., Japkowicz, N., & Viktor, H. (2022). Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods. ↩
63. 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. ↩
64. Willison, S. (2022). Prompt injection attacks against GPT-3. https://simonwillison.net/2022/Sep/12/prompt-injection/ ↩
65. 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 ↩
66. Chase, H. (2022). adversarial-prompts. https://github.com/hwchase17/adversarial-prompts ↩
67. Goodside, R. (2022). GPT-3 Prompt Injection Defenses. https://twitter.com/goodside/status/1578278974526222336?s=20&t=3UMZB7ntYhwAk3QLpKMAbw ↩
68. Mark, C. (2022). Talking to machines: prompt engineering & injection. https://artifact-research.com/artificial-intelligence/talking-to-machines-prompt-engineering-injection/ ↩
69. Selvi, J. (2022). Exploring Prompt Injection Attacks. https://research.nccgroup.com/2022/12/05/exploring-prompt-injection-attacks/ ↩
70. Stuart Armstrong, R. G. (2022). Using GPT-Eliezer against ChatGPT Jailbreaking. https://www.alignmentforum.org/posts/pNcFYZnPdXyL2RfgA/using-gpt-eliezer-against-chatgpt-jailbreaking ↩
71. The entire prompt of Microsoft Bing Chat?! (Hi, Sydney.). (2023). https://twitter.com/kliu128/status/1623472922374574080 ↩
72. Perez, F., & Ribeiro, I. (2022). Ignore Previous Prompt: Attack Techniques For Language Models. arXiv. https://doi.org/10.48550/ARXIV.2211.09527 ↩
73. Brundage, M. (2022). Lessons learned on Language Model Safety and misuse. In OpenAI. OpenAI. https://openai.com/blog/language-model-safety-and-misuse/ ↩
74. Wang, Y.-S., & Chang, Y. (2022). Toxicity Detection with Generative Prompt-based Inference. arXiv. https://doi.org/10.48550/ARXIV.2205.12390 ↩
75. Markov, T. (2022). New and improved content moderation tooling. In OpenAI. OpenAI. https://openai.com/blog/new-and-improved-content-moderation-tooling/ ↩
76. (2022). https://beta.openai.com/docs/guides/moderation ↩
77. (2022). https://openai.com/blog/chatgpt/ ↩
78. ok I saw a few people jailbreaking safeguards openai put on chatgpt so I had to give it a shot myself. (2022). https://twitter.com/alicemazzy/status/1598288519301976064 ↩
79. Bypass @OpenAI’s ChatGPT alignment efforts with this one weird trick. (2022). https://twitter.com/m1guelpf/status/1598203861294252033 ↩
80. ChatGPT jailbreaking itself. (2022). https://twitter.com/haus_cole/status/1598541468058390534 ↩
81. Using “pretend” on #ChatGPT can do some wild stuff. You can kind of get some insight on the future, alternative universe. (2022). https://twitter.com/NeroSoares/status/1608527467265904643 ↩
82. I kinda like this one even more! (2022). https://twitter.com/NickEMoran/status/1598101579626057728 ↩
83. Degrave, J. (2022). Building A Virtual Machine inside ChatGPT. Engraved. https://www.engraved.blog/building-a-virtual-machine-inside/ ↩
84. (2022). https://www.sudo.ws/ ↩
85. Perez, F., & Ribeiro, I. (2022). Ignore Previous Prompt: Attack Techniques For Language Models. arXiv. https://doi.org/10.48550/ARXIV.2211.09527 ↩
86. AIWithVibes. (2023). 7 ChatGPT JailBreaks and Content Filters Bypass that work. https://chatgpt-jailbreak.super.site/ ↩
87. Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2022). Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing. ACM Computing Surveys. https://doi.org/10.1145/3560815 ↩
88. PromptPapers. (2022). https://github.com/thunlp/PromptPapers ↩
89. 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. ↩
90. 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. ↩
91. 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. ↩
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