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๐Ÿ“š 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.

๐Ÿ”ต = 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 Strategiesโ€‹

Chain of Thought1 ๐Ÿ”ตโ€‹

Zero Shot Chain of Thought2 ๐Ÿ”ตโ€‹

Self Consistency3 ๐Ÿ”ตโ€‹

What Makes Good In-Context Examples for GPT-3?4 ๐Ÿ”ตโ€‹

Ask-Me-Anything Prompting5 ๐Ÿ”ตโ€‹

Generated Knowledge6 ๐Ÿ”ตโ€‹

Recitation-Augmented Language Models7 ๐Ÿ”ตโ€‹

Rethinking the role of demonstrations8 ๐Ÿ”ตโ€‹

Scratchpads9โ€‹

Maieutic Prompting10โ€‹

STaR11โ€‹

Least to Most12 ๐Ÿ”ตโ€‹

Reframing Instructional Prompts to GPTkโ€™s Language13 ๐Ÿ”ตโ€‹

The Turking Test: Can Language Models Understand Instructions?14 ๐Ÿ”ตโ€‹

Reliabilityโ€‹

MathPrompter15 ๐Ÿ”ตโ€‹

The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning16 ๐Ÿ”ตโ€‹

Prompting GPT-3 to be reliable17โ€‹

Diverse Prompts18 ๐Ÿ”ตโ€‹

Calibrate Before Use: Improving Few-Shot Performance of Language Models19 ๐Ÿ”ตโ€‹

Enhanced Self Consistency20โ€‹

Bias and Toxicity in Zero-Shot CoT21 ๐Ÿ”ตโ€‹

Constitutional AI: Harmlessness from AI Feedback22 ๐Ÿ”ตโ€‹

Compositional Generalization - SCAN23โ€‹

Automated Prompt Engineeringโ€‹

AutoPrompt24 ๐Ÿ”ตโ€‹

Automatic Prompt Engineer25โ€‹

Modelsโ€‹

Language Modelsโ€‹

GPT-326 ๐Ÿ”ตโ€‹

GPT-3 Instruct27 ๐Ÿ”ตโ€‹

PaLM28 ๐Ÿ”ตโ€‹

BLOOM29 ๐Ÿ”ตโ€‹

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

Jurassic 131 ๐Ÿ”ตโ€‹

GPT-J-6B32โ€‹

Roberta33โ€‹

Image Modelsโ€‹

Stable Diffusion34 ๐Ÿ”ตโ€‹

DALLE35 ๐Ÿ”ตโ€‹

Soft Promptingโ€‹

Soft Prompting36 ๐Ÿ”ตโ€‹

Interpretable Discretized Soft Prompts37 ๐Ÿ”ตโ€‹

Datasetsโ€‹

MultiArith38 ๐Ÿ”ตโ€‹

GSM8K39 ๐Ÿ”ตโ€‹

HotPotQA40 ๐Ÿ”ตโ€‹

Fever41 ๐Ÿ”ตโ€‹

BBQ: A Hand-Built Bias Benchmark for Question Answering42 ๐Ÿ”ตโ€‹

Image Prompt Engineeringโ€‹

Taxonomy of prompt modifiers43โ€‹

DiffusionDB44โ€‹

The DALLE 2 Prompt Book45 ๐Ÿ”ตโ€‹

Prompt Engineering for Text-Based Generative Art46 ๐Ÿ”ตโ€‹

With the right prompt, Stable Diffusion 2.0 can do hands.47 ๐Ÿ”ตโ€‹

Optimizing Prompts for Text-to-Image Generation48โ€‹

Prompt Engineering IDEsโ€‹

Prompt IDE49 ๐Ÿ”ตโ€‹

Prompt Source50 ๐Ÿ”ตโ€‹

PromptChainer51 ๐Ÿ”ตโ€‹

PromptMaker52 ๐Ÿ”ตโ€‹

Toolingโ€‹

LangChain53 ๐Ÿ”ตโ€‹

TextBox 2.0: A Text Generation Library with Pre-trained Language Models54 ๐Ÿ”ตโ€‹

OpenPrompt: An Open-source Framework for Prompt-learning55 ๐Ÿ”ตโ€‹

GPT Index56 ๐Ÿ”ตโ€‹

Applied Prompt Engineeringโ€‹

Language Model Cascades57โ€‹

MRKL58 ๐Ÿ”ตโ€‹

ReAct59 ๐Ÿ”ตโ€‹

PAL: Program-aided Language Models60 ๐Ÿ”ตโ€‹

User Interface Designโ€‹

Design Guidelines for Prompt Engineering Text-to-Image Generative Models61โ€‹

Prompt Injectionโ€‹

Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods62 ๐Ÿ”ตโ€‹

Evaluating the Susceptibility of Pre-Trained Language Models via Handcrafted Adversarial Examples63 ๐Ÿ”ตโ€‹

Prompt injection attacks against GPT-364 ๐Ÿ”ตโ€‹

Exploiting GPT-3 prompts with malicious inputs that order the model to ignore its previous directions65 ๐Ÿ”ตโ€‹

adversarial-prompts66 ๐Ÿ”ตโ€‹

GPT-3 Prompt Injection Defenses67 ๐Ÿ”ตโ€‹

Talking to machines: prompt engineering & injection68โ€‹

Exploring Prompt Injection Attacks69 ๐Ÿ”ตโ€‹

Using GPT-Eliezer against ChatGPT Jailbreaking70 ๐Ÿ”ตโ€‹

Microsoft Bing Chat Prompt71โ€‹

Jailbreakingโ€‹

Ignore Previous Prompt: Attack Techniques For Language Models72โ€‹

Lessons learned on Language Model Safety and misuse73โ€‹

Toxicity Detection with Generative Prompt-based Inference74โ€‹

New and improved content moderation tooling75โ€‹

OpenAI API76 ๐Ÿ”ตโ€‹

OpenAI ChatGPT77 ๐Ÿ”ตโ€‹

ChatGPT 4 Tweet78 ๐Ÿ”ตโ€‹

Acting Tweet79 ๐Ÿ”ตโ€‹

Research Tweet80 ๐Ÿ”ตโ€‹

Pretend Ability Tweet81 ๐Ÿ”ตโ€‹

Responsibility Tweet82 ๐Ÿ”ตโ€‹

Lynx Mode Tweet83 ๐Ÿ”ตโ€‹

Sudo Mode Tweet84 ๐Ÿ”ตโ€‹

Ignore Previous Prompt85 ๐Ÿ”ตโ€‹

Updated Jailbreaking Prompts86 ๐Ÿ”ตโ€‹

Surveysโ€‹

Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing87โ€‹

PromptPapers88โ€‹

Dataset Generationโ€‹

Discovering Language Model Behaviors with Model-Written Evaluations89โ€‹

Selective Annotation Makes Language Models Better Few-Shot Learners90โ€‹

Applicationsโ€‹

Atlas: Few-shot Learning with Retrieval Augmented Language Models91โ€‹

STRUDEL: Structured Dialogue Summarization for Dialogue Comprehension92โ€‹

Misclโ€‹

Prompting Is Programming: A Query Language For Large Language Models93โ€‹

Parallel Context Windows Improve In-Context Learning of Large Language Models94โ€‹

Learning to Perform Complex Tasks through Compositional Fine-Tuning of Language Models95โ€‹

Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks96โ€‹

Making Pre-trained Language Models Better Few-shot Learners97โ€‹

Grounding with search results98โ€‹

How to Prompt? Opportunities and Challenges of Zero- and Few-Shot Learning for Human-AI Interaction in Creative Applications of Generative Models99โ€‹

On Measuring Social Biases in Prompt-Based Multi-Task Learning100โ€‹

Plot Writing From Pre-Trained Language Models101 ๐Ÿ”ตโ€‹

StereoSet: Measuring stereotypical bias in pretrained language models102โ€‹

Survey of Hallucination in Natural Language Generation103โ€‹

Examples104โ€‹

Wordcraft105โ€‹

PainPoints106โ€‹

Self-Instruct: Aligning Language Model with Self Generated Instructions107โ€‹

From Images to Textual Prompts: Zero-shot VQA with Frozen Large Language Models108โ€‹

Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference109โ€‹

Ask-Me-Anything Prompting5โ€‹

A Watermark for Large Language Models110โ€‹


  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. โ†ฉ
  2. Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., & Iwasawa, Y. (2022). Large Language Models are Zero-Shot Reasoners. โ†ฉ
  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. โ†ฉ
  4. Liu, J., Shen, D., Zhang, Y., Dolan, B., Carin, L., & Chen, W. (2021). What Makes Good In-Context Examples for GPT-3? โ†ฉ
  5. Arora, S., Narayan, A., Chen, M. F., Orr, L., Guha, N., Bhatia, K., Chami, I., Sala, F., & Rรฉ, C. (2022). Ask Me Anything: A simple strategy for prompting language models. โ†ฉ
  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. โ†ฉ
  7. Sun, Z., Wang, X., Tay, Y., Yang, Y., & Zhou, D. (2022). Recitation-Augmented Language Models. โ†ฉ
  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? โ†ฉ
  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. โ†ฉ
  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. โ†ฉ
  11. Zelikman, E., Wu, Y., Mu, J., & Goodman, N. D. (2022). STaR: Bootstrapping Reasoning With Reasoning. โ†ฉ
  12. Zhou, D., Schรคrli, 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. โ†ฉ
  13. Mishra, S., Khashabi, D., Baral, C., Choi, Y., & Hajishirzi, H. (2022). Reframing Instructional Prompts to GPTkโ€™s Language. Findings of the Association for Computational Linguistics: ACL 2022. https://doi.org/10.18653/v1/2022.findings-acl.50 โ†ฉ
  14. Efrat, A., & Levy, O. (2020). The Turking Test: Can Language Models Understand Instructions? โ†ฉ
  15. Imani, S., Du, L., & Shrivastava, H. (2023). MathPrompter: Mathematical Reasoning using Large Language Models. โ†ฉ
  16. Ye, X., & Durrett, G. (2022). The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning. โ†ฉ
  17. Si, C., Gan, Z., Yang, Z., Wang, S., Wang, J., Boyd-Graber, J., & Wang, L. (2022). Prompting GPT-3 To Be Reliable. โ†ฉ
  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. โ†ฉ
  19. Zhao, T. Z., Wallace, E., Feng, S., Klein, D., & Singh, S. (2021). Calibrate Before Use: Improving Few-Shot Performance of Language Models. โ†ฉ
  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. โ†ฉ
  21. Shaikh, O., Zhang, H., Held, W., Bernstein, M., & Yang, D. (2022). On Second Thought, Letโ€™s Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning. โ†ฉ
  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., โ€ฆ Kaplan, J. (2022). Constitutional AI: Harmlessness from AI Feedback. โ†ฉ
  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 โ†ฉ
  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 โ†ฉ
  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. โ†ฉ
  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., โ€ฆ Amodei, D. (2020). Language Models are Few-Shot Learners. โ†ฉ
  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. โ†ฉ
  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., โ€ฆ Fiedel, N. (2022). PaLM: Scaling Language Modeling with Pathways. โ†ฉ
  29. Scao, T. L., Fan, A., Akiki, C., Pavlick, E., Iliฤ‡, S., Hesslow, D., Castagnรฉ, R., Luccioni, A. S., Yvon, F., Gallรฉ, M., Tow, J., Rush, A. M., Biderman, S., Webson, A., Ammanamanchi, P. S., Wang, T., Sagot, B., Muennighoff, N., del Moral, A. V., โ€ฆ Wolf, T. (2022). BLOOM: A 176B-Parameter Open-Access Multilingual Language Model. โ†ฉ
  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. โ†ฉ
  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. โ†ฉ
  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 โ†ฉ
  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. โ†ฉ
  34. Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2021). High-Resolution Image Synthesis with Latent Diffusion Models. โ†ฉ
  35. Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., & Chen, M. (2022). Hierarchical Text-Conditional Image Generation with CLIP Latents. โ†ฉ
  36. Lester, B., Al-Rfou, R., & Constant, N. (2021). The Power of Scale for Parameter-Efficient Prompt Tuning. โ†ฉ
  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. โ†ฉ
  38. Roy, S., & Roth, D. (2015). Solving General Arithmetic Word Problems. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 1743โ€“1752. https://doi.org/10.18653/v1/D15-1202 โ†ฉ
  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. โ†ฉ
  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. โ†ฉ
  41. Thorne, J., Vlachos, A., Christodoulopoulos, C., & Mittal, A. (2018). FEVER: a large-scale dataset for Fact Extraction and VERification. โ†ฉ
  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. โ†ฉ
  43. Oppenlaender, J. (2022). A Taxonomy of Prompt Modifiers for Text-To-Image Generation. โ†ฉ
  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. โ†ฉ
  45. Parsons, G. (2022). The DALLE 2 Prompt Book. https://dallery.gallery/the-dalle-2-prompt-book/ โ†ฉ
  46. Oppenlaender, J. (2022). Prompt Engineering for Text-Based Generative Art. โ†ฉ
  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/ โ†ฉ
  48. Hao, Y., Chi, Z., Dong, L., & Wei, F. (2022). Optimizing Prompts for Text-to-Image Generation. โ†ฉ
  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 โ†ฉ
  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., โ€ฆ Rush, A. M. (2022). PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts. โ†ฉ
  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. โ†ฉ
  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 โ†ฉ
  53. Chase, H. (2022). LangChain (0.0.66) [Computer software]. https://github.com/hwchase17/langchain โ†ฉ
  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. โ†ฉ
  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. โ†ฉ
  56. Liu, J. (2022). GPT Index. https://doi.org/10.5281/zenodo.1234 โ†ฉ
  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. โ†ฉ
  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. โ†ฉ
  59. Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., & Cao, Y. (2022). ReAct: Synergizing Reasoning and Acting in Language Models. โ†ฉ
  60. Gao, L., Madaan, A., Zhou, S., Alon, U., Liu, P., Yang, Y., Callan, J., & Neubig, G. (2022). PAL: Program-aided Language Models. โ†ฉ
  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 โ†ฉ
  62. Crothers, E., Japkowicz, N., & Viktor, H. (2022). Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods. โ†ฉ
  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. โ†ฉ
  64. Willison, S. (2022). Prompt injection attacks against GPT-3. https://simonwillison.net/2022/Sep/12/prompt-injection/ โ†ฉ
  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 โ†ฉ
  66. Chase, H. (2022). adversarial-prompts. https://github.com/hwchase17/adversarial-prompts โ†ฉ
  67. Goodside, R. (2022). GPT-3 Prompt Injection Defenses. https://twitter.com/goodside/status/1578278974526222336?s=20&t=3UMZB7ntYhwAk3QLpKMAbw โ†ฉ
  68. Mark, C. (2022). Talking to machines: prompt engineering & injection. https://artifact-research.com/artificial-intelligence/talking-to-machines-prompt-engineering-injection/ โ†ฉ
  69. Selvi, J. (2022). Exploring Prompt Injection Attacks. https://research.nccgroup.com/2022/12/05/exploring-prompt-injection-attacks/ โ†ฉ
  70. Stuart Armstrong, R. G. (2022). Using GPT-Eliezer against ChatGPT Jailbreaking. https://www.alignmentforum.org/posts/pNcFYZnPdXyL2RfgA/using-gpt-eliezer-against-chatgpt-jailbreaking โ†ฉ
  71. The entire prompt of Microsoft Bing Chat?! (Hi, Sydney.). (2023). https://twitter.com/kliu128/status/1623472922374574080 โ†ฉ
  72. Perez, F., & Ribeiro, I. (2022). Ignore Previous Prompt: Attack Techniques For Language Models. arXiv. https://doi.org/10.48550/ARXIV.2211.09527 โ†ฉ
  73. Brundage, M. (2022). Lessons learned on Language Model Safety and misuse. In OpenAI. OpenAI. https://openai.com/blog/language-model-safety-and-misuse/ โ†ฉ
  74. Wang, Y.-S., & Chang, Y. (2022). Toxicity Detection with Generative Prompt-based Inference. arXiv. https://doi.org/10.48550/ARXIV.2205.12390 โ†ฉ
  75. Markov, T. (2022). New and improved content moderation tooling. In OpenAI. OpenAI. https://openai.com/blog/new-and-improved-content-moderation-tooling/ โ†ฉ
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