๐ 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.
๐ต = 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 ๐ตโ
Zero Shot Chain of Thought2 ๐ตโ
Self Consistency3 ๐ตโ
What Makes Good In-Context Examples for GPT-3?4 ๐ตโ
Ask-Me-Anything Prompting5 ๐ตโ
Generated Knowledge6 ๐ตโ
Recitation-Augmented Language Models7 ๐ตโ
Rethinking the role of demonstrations8 ๐ตโ
Scratchpads9โ
Maieutic Prompting10โ
STaR11โ
Least to Most12 ๐ตโ
Reframing Instructional Prompts to GPTkโs Language13 ๐ตโ
The Turking Test: Can Language Models Understand Instructions?14 ๐ตโ
Reliabilityโ
MathPrompter15 ๐ตโ
The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning16 ๐ตโ
Prompting GPT-3 to be reliable17โ
Diverse Prompts18 ๐ตโ
Calibrate Before Use: Improving Few-Shot Performance of Language Models19 ๐ตโ
Enhanced Self Consistency20โ
Bias and Toxicity in Zero-Shot CoT21 ๐ตโ
Constitutional AI: Harmlessness from AI Feedback22 ๐ตโ
Compositional Generalization - SCAN23โ
Automated Prompt Engineeringโ
AutoPrompt24 ๐ตโ
Automatic Prompt Engineer25โ
Modelsโ
Language Modelsโ
GPT-326 ๐ตโ
GPT-3 Instruct27 ๐ตโ
PaLM28 ๐ตโ
BLOOM29 ๐ตโ
BLOOM+1 (more languages/ 0 shot improvements)30โ
Jurassic 131 ๐ตโ
GPT-J-6B32โ
Roberta33โ
Image Modelsโ
Stable Diffusion34 ๐ตโ
DALLE35 ๐ตโ
Soft Promptingโ
Soft Prompting36 ๐ตโ
Interpretable Discretized Soft Prompts37 ๐ตโ
Datasetsโ
MultiArith38 ๐ตโ
GSM8K39 ๐ตโ
HotPotQA40 ๐ตโ
Fever41 ๐ตโ
BBQ: A Hand-Built Bias Benchmark for Question Answering42 ๐ตโ
Image Prompt Engineeringโ
Taxonomy of prompt modifiers43โ
DiffusionDB44โ
The DALLE 2 Prompt Book45 ๐ตโ
Prompt Engineering for Text-Based Generative Art46 ๐ตโ
With the right prompt, Stable Diffusion 2.0 can do hands.47 ๐ตโ
Optimizing Prompts for Text-to-Image Generation48โ
Prompt Engineering IDEsโ
Prompt IDE49 ๐ตโ
Prompt Source50 ๐ตโ
PromptChainer51 ๐ตโ
PromptMaker52 ๐ตโ
Toolingโ
LangChain53 ๐ตโ
TextBox 2.0: A Text Generation Library with Pre-trained Language Models54 ๐ตโ
OpenPrompt: An Open-source Framework for Prompt-learning55 ๐ตโ
GPT Index56 ๐ตโ
Applied Prompt Engineeringโ
Language Model Cascades57โ
MRKL58 ๐ตโ
ReAct59 ๐ตโ
PAL: Program-aided Language Models60 ๐ตโ
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 ๐ตโ
Evaluating the Susceptibility of Pre-Trained Language Models via Handcrafted Adversarial Examples63 ๐ตโ
Prompt injection attacks against GPT-364 ๐ตโ
Exploiting GPT-3 prompts with malicious inputs that order the model to ignore its previous directions65 ๐ตโ
adversarial-prompts66 ๐ตโ
GPT-3 Prompt Injection Defenses67 ๐ตโ
Talking to machines: prompt engineering & injection68โ
Exploring Prompt Injection Attacks69 ๐ตโ
Using GPT-Eliezer against ChatGPT Jailbreaking70 ๐ตโ
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 ๐ตโ
OpenAI ChatGPT77 ๐ตโ
ChatGPT 4 Tweet78 ๐ตโ
Acting Tweet79 ๐ตโ
Research Tweet80 ๐ตโ
Pretend Ability Tweet81 ๐ตโ
Responsibility Tweet82 ๐ตโ
Lynx Mode Tweet83 ๐ตโ
Sudo Mode Tweet84 ๐ตโ
Ignore Previous Prompt85 ๐ตโ
Updated Jailbreaking Prompts86 ๐ตโ
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 ๐ตโ
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
- 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. โฉ
- Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., & Iwasawa, Y. (2022). Large Language Models are Zero-Shot Reasoners. โฉ
- 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. โฉ
- Liu, J., Shen, D., Zhang, Y., Dolan, B., Carin, L., & Chen, W. (2021). What Makes Good In-Context Examples for GPT-3? โฉ
- 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. โฉ
- Liu, J., Liu, A., Lu, X., Welleck, S., West, P., Bras, R. L., Choi, Y., & Hajishirzi, H. (2021). Generated Knowledge Prompting for Commonsense Reasoning. โฉ
- Sun, Z., Wang, X., Tay, Y., Yang, Y., & Zhou, D. (2022). Recitation-Augmented Language Models. โฉ
- 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? โฉ
- 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. โฉ
- Jung, J., Qin, L., Welleck, S., Brahman, F., Bhagavatula, C., Bras, R. L., & Choi, Y. (2022). Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations. โฉ
- Zelikman, E., Wu, Y., Mu, J., & Goodman, N. D. (2022). STaR: Bootstrapping Reasoning With Reasoning. โฉ
- 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. โฉ
- Mishra, S., Khashabi, D., Baral, C., Choi, Y., & Hajishirzi, H. (2022). Reframing Instructional Prompts to GPTkโs Language. Findings of the Association for Computational Linguistics: ACL 2022. https://doi.org/10.18653/v1/2022.findings-acl.50 โฉ
- Efrat, A., & Levy, O. (2020). The Turking Test: Can Language Models Understand Instructions? โฉ
- Imani, S., Du, L., & Shrivastava, H. (2023). MathPrompter: Mathematical Reasoning using Large Language Models. โฉ
- Ye, X., & Durrett, G. (2022). The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning. โฉ
- Si, C., Gan, Z., Yang, Z., Wang, S., Wang, J., Boyd-Graber, J., & Wang, L. (2022). Prompting GPT-3 To Be Reliable. โฉ
- 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. โฉ
- Zhao, T. Z., Wallace, E., Feng, S., Klein, D., & Singh, S. (2021). Calibrate Before Use: Improving Few-Shot Performance of Language Models. โฉ
- 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. โฉ
- Shaikh, O., Zhang, H., Held, W., Bernstein, M., & Yang, D. (2022). On Second Thought, Letโs Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning. โฉ
- 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., โฆ Kaplan, J. (2022). Constitutional AI: Harmlessness from AI Feedback. โฉ
- 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 โฉ
- 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 โฉ
- Zhou, Y., Muresanu, A. I., Han, Z., Paster, K., Pitis, S., Chan, H., & Ba, J. (2022). Large Language Models Are Human-Level Prompt Engineers. โฉ
- 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., โฆ Amodei, D. (2020). Language Models are Few-Shot Learners. โฉ
- 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. โฉ
- 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., โฆ Fiedel, N. (2022). PaLM: Scaling Language Modeling with Pathways. โฉ
- 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., โฆ Wolf, T. (2022). BLOOM: A 176B-Parameter Open-Access Multilingual Language Model. โฉ
- 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. โฉ
- 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. โฉ
- 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 โฉ
- 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. โฉ
- Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2021). High-Resolution Image Synthesis with Latent Diffusion Models. โฉ
- Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., & Chen, M. (2022). Hierarchical Text-Conditional Image Generation with CLIP Latents. โฉ
- Lester, B., Al-Rfou, R., & Constant, N. (2021). The Power of Scale for Parameter-Efficient Prompt Tuning. โฉ
- 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. โฉ
- Roy, S., & Roth, D. (2015). Solving General Arithmetic Word Problems. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 1743โ1752. https://doi.org/10.18653/v1/D15-1202 โฉ
- 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. โฉ
- 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. โฉ
- Thorne, J., Vlachos, A., Christodoulopoulos, C., & Mittal, A. (2018). FEVER: a large-scale dataset for Fact Extraction and VERification. โฉ
- 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. โฉ
- Oppenlaender, J. (2022). A Taxonomy of Prompt Modifiers for Text-To-Image Generation. โฉ
- 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. โฉ
- Parsons, G. (2022). The DALLE 2 Prompt Book. https://dallery.gallery/the-dalle-2-prompt-book/ โฉ
- Oppenlaender, J. (2022). Prompt Engineering for Text-Based Generative Art. โฉ
- 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/ โฉ
- Hao, Y., Chi, Z., Dong, L., & Wei, F. (2022). Optimizing Prompts for Text-to-Image Generation. โฉ
- 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 โฉ
- 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., โฆ Rush, A. M. (2022). PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts. โฉ
- 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. โฉ
- 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 โฉ
- Chase, H. (2022). LangChain (0.0.66) [Computer software]. https://github.com/hwchase17/langchain โฉ
- 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. โฉ
- 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. โฉ
- Liu, J. (2022). GPT Index. https://doi.org/10.5281/zenodo.1234 โฉ
- 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. โฉ
- 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. โฉ
- Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., & Cao, Y. (2022). ReAct: Synergizing Reasoning and Acting in Language Models. โฉ
- Gao, L., Madaan, A., Zhou, S., Alon, U., Liu, P., Yang, Y., Callan, J., & Neubig, G. (2022). PAL: Program-aided Language Models. โฉ
- 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 โฉ
- Crothers, E., Japkowicz, N., & Viktor, H. (2022). Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods. โฉ
- 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. โฉ
- Willison, S. (2022). Prompt injection attacks against GPT-3. https://simonwillison.net/2022/Sep/12/prompt-injection/ โฉ
- 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 โฉ
- Chase, H. (2022). adversarial-prompts. https://github.com/hwchase17/adversarial-prompts โฉ
- Goodside, R. (2022). GPT-3 Prompt Injection Defenses. https://twitter.com/goodside/status/1578278974526222336?s=20&t=3UMZB7ntYhwAk3QLpKMAbw โฉ
- Mark, C. (2022). Talking to machines: prompt engineering & injection. https://artifact-research.com/artificial-intelligence/talking-to-machines-prompt-engineering-injection/ โฉ
- Selvi, J. (2022). Exploring Prompt Injection Attacks. https://research.nccgroup.com/2022/12/05/exploring-prompt-injection-attacks/ โฉ
- Stuart Armstrong, R. G. (2022). Using GPT-Eliezer against ChatGPT Jailbreaking. https://www.alignmentforum.org/posts/pNcFYZnPdXyL2RfgA/using-gpt-eliezer-against-chatgpt-jailbreaking โฉ
- The entire prompt of Microsoft Bing Chat?! (Hi, Sydney.). (2023). https://twitter.com/kliu128/status/1623472922374574080 โฉ
- Perez, F., & Ribeiro, I. (2022). Ignore Previous Prompt: Attack Techniques For Language Models. arXiv. https://doi.org/10.48550/ARXIV.2211.09527 โฉ
- Brundage, M. (2022). Lessons learned on Language Model Safety and misuse. In OpenAI. OpenAI. https://openai.com/blog/language-model-safety-and-misuse/ โฉ
- Wang, Y.-S., & Chang, Y. (2022). Toxicity Detection with Generative Prompt-based Inference. arXiv. https://doi.org/10.48550/ARXIV.2205.12390 โฉ
- Markov, T. (2022). New and improved content moderation tooling. In OpenAI. OpenAI. https://openai.com/blog/new-and-improved-content-moderation-tooling/ โฉ
- (2022). https://beta.openai.com/docs/guides/moderation โฉ
- (2022). https://openai.com/blog/chatgpt/ โฉ
- 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 โฉ
- Bypass @OpenAIโs ChatGPT alignment efforts with this one weird trick. (2022). https://twitter.com/m1guelpf/status/1598203861294252033 โฉ
- ChatGPT jailbreaking itself. (2022). https://twitter.com/haus_cole/status/1598541468058390534 โฉ
- Using โpretendโ 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 โฉ
- I kinda like this one even more! (2022). https://twitter.com/NickEMoran/status/1598101579626057728 โฉ
- Degrave, J. (2022). Building A Virtual Machine inside ChatGPT. Engraved. https://www.engraved.blog/building-a-virtual-machine-inside/ โฉ
- (2022). https://www.sudo.ws/ โฉ
- Perez, F., & Ribeiro, I. (2022). Ignore Previous Prompt: Attack Techniques For Language Models. arXiv. https://doi.org/10.48550/ARXIV.2211.09527 โฉ
- AIWithVibes. (2023). 7 ChatGPT JailBreaks and Content Filters Bypass that work. https://chatgpt-jailbreak.super.site/ โฉ
- 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 โฉ
- PromptPapers. (2022). https://github.com/thunlp/PromptPapers โฉ
- Perez, E., Ringer, S., Lukoลกiลซtฤ, 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., โฆ Kaplan, J. (2022). Discovering Language Model Behaviors with Model-Written Evaluations. โฉ
- 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. โฉ
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