Bibliography

Reading Time: 5 minutes

Last updated on August 7, 2024

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.

AUTOGENERATED BELOW, DO NOT EDIT

Agents

MRKL

ReAct

PAL

Auto-GPT

Baby AGI

AgentGPT

Toolformer

Automated

AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts

automatic prompt engineer

Soft Prompting

discretized soft prompting (interpreting)

Datasets

SCAN dataset (compositional generalization)

GSM8K

hotpotQA

multiarith

fever dataset

bbq

Detection

Don't ban chatgpt in schools. teach with it.

Schools Shouldn't Ban Access to ChatGPT

Certified Neural Network Watermarks with Randomized Smoothing

Watermarking Pre-trained Language Models with Backdooring

GW preparing disciplinary response to AI programs as faculty explore educational use

A Watermark for Large Language Models

DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature

Image Prompt Engineering

Prompt Engineering for Text-Based Generative Art

The DALLE 2 Prompt Book

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

Meta Analysis

How Generative AI Is Changing Creative Work

How AI Will Change the Workplace

ChatGPT took their jobs. Now they walk dogs and fix air conditioners.

No title

Miscl

The Turking Test: Can Language Models Understand Instructions?

A Taxonomy of Prompt Modifiers for Text-To-Image Generation

DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Models

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

Plot Writing From Pre-Trained Language Models

{S}tereo{S}et: Measuring stereotypical bias in pretrained language models

Survey of Hallucination in Natural Language Generation

Wordcraft: Story Writing With Large Language Models

PainPoints: A Framework for Language-based Detection of Chronic Pain and Expert-Collaborative Text-Summarization

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

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

Human-level concept learning through probabilistic program induction

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

How to use OpenAI’s ChatGPT to write the perfect cold email

Cacti: biology and uses

Are Language Models Worse than Humans at Following Prompts? It’s Complicated

Unleashing Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration

Prompt Hacking

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

New jailbreak based on virtual functions - smuggle illegal tokens to the 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

The entire prompt of Microsoft Bing Chat?! (Hi, 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 I saw a few people jailbreaking safeguards openai put on chatgpt so I had to give it a shot myself

Bypass @OpenAI's ChatGPT alignment efforts with this one weird trick

ChatGPT jailbreaking itself

Using "pretend" on #ChatGPT can do some wild stuff. You can kind of get some insight on the future, alternative universe.

I kinda like this one even more!

uh oh

Building A Virtual Machine inside ChatGPT

Reliability

MathPrompter: Mathematical Reasoning using Large Language Models

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

Prompting GPT-3 To Be Reliable

On the Advance of Making Language Models Better Reasoners

Ask Me Anything: A simple strategy for prompting language models

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

Can large language models reason about medical questions?

Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inference

On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning

Evaluating language models can be tricky

Constitutional AI: Harmlessness from AI Feedback

Surveys

Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition

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

Techniques

Chain of Thought Prompting Elicits Reasoning in Large Language Models

Large Language Models are Zero-Shot Reasoners

Self-Consistency Improves Chain of Thought Reasoning in Language Models

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

Generated Knowledge Prompting for Commonsense Reasoning

Recitation-Augmented Language Models

Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?

Show Your Work: Scratchpads for Intermediate Computation with Language Models

Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations

STaR: Bootstrapping Reasoning With Reasoning

Least-to-Most Prompting Enables Complex Reasoning in Large Language Models

Reframing Instructional Prompts to GPTk’s Language

Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language Models

Role-Play with Large Language Models

CAMEL: Communicative Agents for "Mind" Exploration of Large Scale Language Model Society

TELeR: A General Taxonomy of LLM Prompts for Benchmarking Complex Tasks

Models

Image Models

Stable Diffusion

DALLE

Language Models

ChatGPT

GPT-3

Instruct GPT

GPT-4

PaLM: Scaling Language Modeling with Pathways

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

BLOOM+1: Adding Language Support to BLOOM for Zero-Shot Prompting

Jurassic-1: Technical Details and Evaluation, White paper, AI21 Labs, 2021

GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model

Roberta: A robustly optimized bert pretraining approach

Tooling

Ides

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

Interactive and Visual Prompt Engineering for Ad-hoc Task Adaptation with Large Language Models

PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts

PromptChainer: Chaining Large Language Model Prompts through Visual Programming

OpenPrompt: An Open-source Framework for Prompt-learning

PromptMaker: Prompt-Based Prototyping with 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

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). ↩
Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., & Cao, Y. (2022). ↩
Gao, L., Madaan, A., Zhou, S., Alon, U., Liu, P., Yang, Y., Callan, J., & Neubig, G. (2022). ↩
Significant-Gravitas. (2023). https://news.agpt.co/ ↩
Nakajima, Y. (2023). https://github.com/yoheinakajima/babyagi ↩
Reworkd.ai. (2023). https://github.com/reworkd/AgentGPT ↩
Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Zettlemoyer, L., Cancedda, N., & Scialom, T. (2023). ↩
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. ↩
Zhou, Y., Muresanu, A. I., Han, Z., Paster, K., Pitis, S., Chan, H., & Ba, J. (2022). Large Language Models Are Human-Level Prompt Engineers. ↩
Lester, B., Al-Rfou, R., & Constant, N. (2021). The Power of Scale for Parameter-Efficient Prompt Tuning. ↩
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. ↩
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 ↩
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. ↩
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. ↩
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 ↩
Thorne, J., Vlachos, A., Christodoulopoulos, C., & Mittal, A. (2018). FEVER: a large-scale dataset for Fact Extraction and VERification. ↩
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. ↩
Roose, K. (2022). Don’t ban chatgpt in schools. teach with it. https://www.nytimes.com/2023/01/12/technology/chatgpt-schools-teachers.html ↩
Lipman, J., & Distler, R. (2023). Schools Shouldn’t Ban Access to ChatGPT. https://time.com/6246574/schools-shouldnt-ban-access-to-chatgpt/ ↩
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. ↩
Gu, C., Huang, C., Zheng, X., Chang, K.-W., & Hsieh, C.-J. (2022). Watermarking Pre-trained Language Models with Backdooring. ↩
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/ ↩
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 ↩
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 ↩
Oppenlaender, J. (2022). Prompt Engineering for Text-Based Generative Art. ↩
Parsons, G. (2022). The DALLE 2 Prompt Book. https://dallery.gallery/the-dalle-2-prompt-book/ ↩
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/ ↩
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 ↩
Captain, S. (2023). How AI Will Change the Workplace. Wall Street Journal. https://www.wsj.com/articles/how-ai-change-workplace-af2162ee ↩
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/ ↩
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 ↩
Efrat, A., & Levy, O. (2020). The Turking Test: Can Language Models Understand Instructions? ↩
Oppenlaender, J. (2022). A Taxonomy of Prompt Modifiers for Text-To-Image Generation. ↩
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. ↩
Hao, Y., Chi, Z., Dong, L., & Wei, F. (2022). Optimizing Prompts for Text-to-Image Generation. ↩
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. ↩
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 ↩
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. ↩
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. ↩
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. ↩
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. ↩
Beurer-Kellner, L., Fischer, M., & Vechev, M. (2022). Prompting Is Programming: A Query Language For Large Language Models. ↩
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. ↩
Bursztyn, V. S., Demeter, D., Downey, D., & Birnbaum, L. (2022). Learning to Perform Complex Tasks through Compositional Fine-Tuning of Language Models. ↩
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. ↩
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 ↩
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. ↩
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. ↩
Jin, Y., Kadam, V., & Wanvarie, D. (2022). Plot Writing From Pre-Trained Language Models. ↩
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 ↩
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 ↩
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. ↩
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. ↩
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. ↩
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. ↩
Markov, T. (2022). New and improved content moderation tooling. In OpenAI. OpenAI. https://openai.com/blog/new-and-improved-content-moderation-tooling/ ↩
Schick, T., & Schütze, H. (2020). Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference. ↩
Lake, B. M., Salakhutdinov, R., & Tenenbaum, J. B. (2015). Human-level concept learning through probabilistic program induction. Science, 350(6266), 1332–1338. ↩
Forsgren, S., & Martiros, H. (2022). Riffusion - Stable diffusion for real-time music generation. https://riffusion.com/about ↩
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 ↩
Nobel, P. S., & others. (2002). Cacti: biology and uses. Univ of California Press. ↩
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]. ↩
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. ↩
Crothers, E., Japkowicz, N., & Viktor, H. (2022). Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods. ↩
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 ↩
Kang, D., Li, X., Stoica, I., Guestrin, C., Zaharia, M., & Hashimoto, T. (2023). Exploiting Programmatic Behavior of LLMs: Dual-Use Through Standard Security Attacks. ↩
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. ↩
KIHO, L. (2023). ChatGPT “DAN” (and other “Jailbreaks”). https://github.com/0xk1h0/ChatGPT_DAN ↩
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. ↩
Willison, S. (2022). Prompt injection attacks against GPT-3. https://simonwillison.net/2022/Sep/12/prompt-injection/ ↩
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 ↩
Goodside, R. (2023). History Correction. https://twitter.com/goodside/status/1610110111791325188?s=20&t=ulviQABPXFIIt4ZNZPAUCQ ↩
Chase, H. (2022). adversarial-prompts. https://github.com/hwchase17/adversarial-prompts ↩
Goodside, R. (2022). GPT-3 Prompt Injection Defenses. https://twitter.com/goodside/status/1578278974526222336?s=20&t=3UMZB7ntYhwAk3QLpKMAbw ↩
Mark, C. (2022). Talking to machines: prompt engineering & injection. https://artifact-research.com/artificial-intelligence/talking-to-machines-prompt-engineering-injection/ ↩
Stuart Armstrong, R. G. (2022). Using GPT-Eliezer against ChatGPT Jailbreaking. https://www.alignmentforum.org/posts/pNcFYZnPdXyL2RfgA/using-gpt-eliezer-against-chatgpt-jailbreaking ↩
Selvi, J. (2022). Exploring Prompt Injection Attacks. https://research.nccgroup.com/2022/12/05/exploring-prompt-injection-attacks/ ↩
Liu, K. (2023). The entire prompt of Microsoft Bing Chat?! (Hi, Sydney.). https://twitter.com/kliu128/status/1623472922374574080 ↩
Perez, F., & Ribeiro, I. (2022). Ignore Previous Prompt: Attack Techniques For Language Models. arXiv. https://doi.org/10.48550/ARXIV.2211.09527 ↩
Brundage, M. (2022). Lessons learned on Language Model Safety and misuse. In OpenAI. OpenAI. https://openai.com/blog/language-model-safety-and-misuse/ ↩
Wang, Y.-S., & Chang, Y. (2022). Toxicity Detection with Generative Prompt-based Inference. arXiv. https://doi.org/10.48550/ARXIV.2205.12390 ↩
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 ↩
Piedrafita, M. (2022). Bypass @OpenAI’s ChatGPT alignment efforts with this one weird trick. https://twitter.com/m1guelpf/status/1598203861294252033 ↩
Parfait, D. (2022). ChatGPT jailbreaking itself. https://twitter.com/haus_cole/status/1598541468058390534 ↩
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 ↩
Moran, N. (2022). I kinda like this one even more! https://twitter.com/NickEMoran/status/1598101579626057728 ↩
samczsun. (2022). uh oh. https://twitter.com/samczsun/status/1598679658488217601 ↩
Degrave, J. (2022). Building A Virtual Machine inside ChatGPT. Engraved. https://www.engraved.blog/building-a-virtual-machine-inside/ ↩
Imani, S., Du, L., & Shrivastava, H. (2023). MathPrompter: Mathematical Reasoning using Large Language Models. ↩
Ye, X., & Durrett, G. (2022). The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning. ↩
Si, C., Gan, Z., Yang, Z., Wang, S., Wang, J., Boyd-Graber, J., & Wang, L. (2022). Prompting GPT-3 To Be Reliable. ↩
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. ↩
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. ↩
Zhao, T. Z., Wallace, E., Feng, S., Klein, D., & Singh, S. (2021). Calibrate Before Use: Improving Few-Shot Performance of Language Models. ↩
Liévin, V., Hother, C. E., & Winther, O. (2022). Can large language models reason about medical questions? ↩
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. ↩
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