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******************
AUTOGENERATED: DO NOT MODIFY
******************@misc{karpas2022mrkl,
lp_title={MRKL},
author={Ehud Karpas and Omri Abend and Yonatan Belinkov and Barak Lenz and Opher Lieber and Nir Ratner and Yoav Shoham and Hofit Bata and Yoav Levine and Kevin Leyton-Brown and Dor Muhlgay and Noam Rozen and Erez Schwartz and Gal Shachaf and Shai Shalev-Shwartz and Amnon Shashua and Moshe Tenenholtz},
year={2022},
eprint={2205.00445},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@misc{yao2022react,
lp_title={ReAct},
author={Shunyu Yao and Jeffrey Zhao and Dian Yu and Nan Du and Izhak Shafran and Karthik Narasimhan and Yuan Cao},
year={2022},
eprint={2210.03629},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@misc{gao2022pal,
lp_title={PAL},
author={Luyu Gao and Aman Madaan and Shuyan Zhou and Uri Alon and Pengfei Liu and Yiming Yang and Jamie Callan and Graham Neubig},
year={2022},
eprint={2211.10435},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@article{richards2023,
lp_title={Auto-GPT},
url={https://news.agpt.co/},
author={Significant-Gravitas},
year={2023}
}
@article{nakajima2023,
lp_title={Baby AGI},
url={https://github.com/yoheinakajima/babyagi},
author={Yohei Nakajima},
year={2023}
}
@article{reworkd2023,
lp_title={AgentGPT},
url={https://github.com/reworkd/AgentGPT},
author={Reworkd.ai},
year={2023}
}
@misc{schick2023toolformer,
lp_title={Toolformer},
author={Timo Schick and Jane Dwivedi-Yu and Roberto Dessì and Roberta Raileanu
and Maria Lomeli and Luke Zettlemoyer and Nicola Cancedda and Thomas Scialom},
year={2023},
eprint={2302.04761},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
% Automated Prompt Engineering
% definition of prompting
@article{shin2020autoprompt,
title={AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts},
url={http://dx.doi.org/10.18653/v1/2020.emnlp-main.346},
DOI={10.18653/v1/2020.emnlp-main.346},
journal={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
publisher={Association for Computational Linguistics},
author={Shin, Taylor and Razeghi, Yasaman and Logan IV, Robert L. and Wallace, Eric and Singh, Sameer},
year={2020} }
%
@misc{zhou2022large,
lp_title={automatic prompt engineer},
title={Large Language Models Are Human-Level Prompt Engineers},
author={Yongchao Zhou and Andrei Ioan Muresanu and Ziwen Han and Keiran Paster and Silviu Pitis and Harris Chan and Jimmy Ba},
year={2022},
eprint={2211.01910},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
@misc{lester2021power,
lp_title={Soft Prompting},
title={The Power of Scale for Parameter-Efficient Prompt Tuning},
author={Brian Lester and Rami Al-Rfou and Noah Constant},
year={2021},
eprint={2104.08691},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@misc{khashabi2021prompt,
lp_title={discretized soft prompting (interpreting)},
title={Prompt Waywardness: The Curious Case of Discretized Interpretation of Continuous Prompts},
author={Daniel Khashabi and Shane Lyu and Sewon Min and Lianhui Qin and Kyle Richardson and Sean Welleck and Hannaneh Hajishirzi and Tushar Khot and Ashish Sabharwal and Sameer Singh and Yejin Choi},
year={2021},
eprint={2112.08348},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@misc{lake2018scan,
lp_title={SCAN dataset (compositional generalization)},
title = {Generalization without Systematicity: {{On}} the Compositional Skills of Sequence-to-Sequence Recurrent Networks},
author = {Lake, Brenden M. and Baroni, Marco},
year = {2018},
eprint = {1711.00350},
archivePrefix = {arXiv},
primaryclass = {cs.AI},
doi = {10.48550/arXiv.1711.00350},
}
@misc{cobbe2021training,
lp_title={GSM8K},
title={Training Verifiers to Solve Math Word Problems},
author={Karl Cobbe and Vineet Kosaraju and Mohammad Bavarian and Mark Chen and Heewoo Jun and Lukasz Kaiser and Matthias Plappert and Jerry Tworek and Jacob Hilton and Reiichiro Nakano and Christopher Hesse and John Schulman},
year={2021},
eprint={2110.14168},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
@misc{yang2018hotpotqa,
lp_title={hotpotQA},
title={HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering},
author={Zhilin Yang and Peng Qi and Saizheng Zhang and Yoshua Bengio and William W. Cohen and Ruslan Salakhutdinov and Christopher D. Manning},
year={2018},
eprint={1809.09600},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@inproceedings{roy-roth-2015-solving,
lp_title={multiarith},
title = "Solving General Arithmetic Word Problems",
author = "Roy, Subhro and
Roth, Dan",
booktitle = "Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing",
month = sep,
year = "2015",
address = "Lisbon, Portugal",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D15-1202",
doi = "10.18653/v1/D15-1202",
pages = "1743--1752",
}
@misc{thorne2018fever,
lp_title={fever dataset},
title={FEVER: a large-scale dataset for Fact Extraction and VERification},
author={James Thorne and Andreas Vlachos and Christos Christodoulopoulos and Arpit Mittal},
year={2018},
eprint={1803.05355},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@misc{parrish2021bbq,
lp_title={bbq},
title={BBQ: A Hand-Built Bias Benchmark for Question Answering},
author={Alicia Parrish and Angelica Chen and Nikita Nangia and Vishakh Padmakumar and Jason Phang and Jana Thompson and Phu Mon Htut and Samuel R. Bowman},
year={2021},
eprint={2110.08193},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
% detection of LLM writing
@article{roose2022dont,
title={Don't ban chatgpt in schools. teach with it.},
author={Kevin Roose},
day={12},
month={jan},
year={2022},
url={https://www.nytimes.com/2023/01/12/technology/chatgpt-schools-teachers.html}
}
@article{lipman2022gpt,
title={Schools Shouldn't Ban Access to ChatGPT},
author={Joanne Lipman and Rebecca Distler},
day={11},
month={jan},
year={2023},
url={https://time.com/6246574/schools-shouldnt-ban-access-to-chatgpt/}
}
@misc{bansal2022certified,
title={Certified Neural Network Watermarks with Randomized Smoothing},
author={Arpit Bansal and Ping-yeh Chiang and Michael Curry and Rajiv Jain and Curtis Wigington and Varun Manjunatha and John P Dickerson and Tom Goldstein},
year={2022},
eprint={2207.07972},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
@misc{gu2022watermarking,
title={Watermarking Pre-trained Language Models with Backdooring},
author={Chenxi Gu and Chengsong Huang and Xiaoqing Zheng and Kai-Wei Chang and Cho-Jui Hsieh},
year={2022},
eprint={2210.07543},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@misc{noonan2023gw,
title={GW preparing disciplinary response to AI programs as faculty explore educational use},
author={Eoighan Noonan and Owen Averill},
year={2023},
month={jan},
day={17},
url={https://www.gwhatchet.com/2023/01/17/gw-preparing-disciplinary-response-to-ai-programs-as-faculty-explore-educational-use/}
}
@misc{kirchenbauer2023watermarking,
title={A Watermark for Large Language Models},
author={John Kirchenbauer and Jonas Geiping and Yuxin Wen and Jonathan Katz and Ian Miers and Tom Goldstein},
year={2023},
month={jan},
day={27},
url={https://arxiv.org/abs/2301.10226}
}
@unknown{mitchell2023detectgpt,
author = {Mitchell, Eric and Lee, Yoonho and Khazatsky, Alexander and Manning, Christopher and Finn, Chelsea},
year = {2023},
month = {01},
pages = {},
title = {DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature},
doi = {10.48550/arXiv.2301.11305}
}
% vocab
@unknown{oppenlaender2022prompt,
author = {Oppenlaender, Jonas},
year = {2022},
month = {04},
title = {Prompt Engineering for Text-Based Generative Art}
}
%% image PE guides
@misc{parsons2022dalleprompt,
author={Guy Parsons},
url={https://dallery.gallery/the-dalle-2-prompt-book/},
title={The DALLE 2 Prompt Book},
year={2022},
month={July},
}
% fix hands
@misc{blake2022with,
author={Blake},
url={https://www.reddit.com/r/StableDiffusion/comments/z7salo/with_the_right_prompt_stable_diffusion_20_can_do/},
title={With the right prompt, Stable Diffusion 2.0 can do hands.},
year={2022},
}
@article{Davenport_Mittal_2022, title={How Generative AI Is Changing Creative Work}, ISSN={0017-8012}, url={https://hbr.org/2022/11/how-generative-ai-is-changing-creative-work}, abstractNote={Generative AI models for businesses threaten to upend the world of content creation, with substantial impacts on marketing, software, design, entertainment, and interpersonal communications. These models are able to produce text and images: blog posts, program code, poetry, and artwork. The software uses complex machine learning models to predict the next word based on previous word sequences, or the next image based on words describing previous images. Companies need to understand how these tools work, and how they can add value.}, journal={Harvard Business Review}, author={Davenport, Thomas H. and Mittal, Nitin}, year={2022}, month={Nov} }
@article{Captain_2023, title={How AI Will Change the Workplace}, ISSN={0099-9660}, url={https://www.wsj.com/articles/how-ai-change-workplace-af2162ee}, abstractNote={We asked some top thinkers from different fields to weigh in on what’s ahead, as the AI explosion compels businesses to rethink, well, almost everything.}, journal={Wall Street Journal}, author={Captain, Seán}, year={2023}, month={May}, language={en-US} }
@article{Verma_Vynck_2023, title={ChatGPT took their jobs. Now they walk dogs and fix air conditioners.}, ISSN={0190-8286}, url={https://www.washingtonpost.com/technology/2023/06/02/ai-taking-jobs/}, abstractNote={Meet the people who have already lost their jobs to AI.}, journal={Washington Post}, author={Verma, Pranshu and Vynck, Gerrit De}, year={2023}, month={Jun}, language={en-US} }
@article{IBM_Do_2023, url={https://www.bloomberg.com/news/articles/2023-05-01/ibm-to-pause-hiring-for-back-office-jobs-that-ai-could-kill}, abstractNote={International Business Machines Corp. Chief Executive Officer Arvind Krishna said the company expects to pause hiring for roles it thinks could be replaced with artificial intelligence in the coming years.}, journal={Bloomberg.com}, year={2023}, month={May}, language={en}, author= {Ford, Brody}}
@misc{efrat2020turking,
title={The Turking Test: Can Language Models Understand Instructions?},
author={Avia Efrat and Omer Levy},
year={2020},
eprint={2010.11982},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
%% Image Prompt Engineering
@misc{oppenlaender2022taxonomy,
title={A Taxonomy of Prompt Modifiers for Text-To-Image Generation},
author={Jonas Oppenlaender},
year={2022},
eprint={2204.13988},
archivePrefix={arXiv},
primaryClass={cs.MM}
}
@misc{wang2022diffusiondb,
title={DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Models},
author={Zijie J. Wang and Evan Montoya and David Munechika and Haoyang Yang and Benjamin Hoover and Duen Horng Chau},
year={2022},
eprint={2210.14896},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
@misc{hao2022optimizing,
title={Optimizing Prompts for Text-to-Image Generation},
author={Yaru Hao and Zewen Chi and Li Dong and Furu Wei},
year={2022},
eprint={2212.09611},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
% Applied Prompt Engineering
% cascades
@misc{dohan2022language,
title={Language Model Cascades},
author={David Dohan and Winnie Xu and Aitor Lewkowycz and Jacob Austin and David Bieber and Raphael Gontijo Lopes and Yuhuai Wu and Henryk Michalewski and Rif A. Saurous and Jascha Sohl-dickstein and Kevin Murphy and Charles Sutton},
year={2022},
eprint={2207.10342},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
% User Interface Design
@inproceedings{liu2022design,
author = {Liu, Vivian and Chilton, Lydia B},
title = {Design Guidelines for Prompt Engineering Text-to-Image Generative Models},
year = {2022},
isbn = {9781450391573},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3491102.3501825},
doi = {10.1145/3491102.3501825},
abstract = {Text-to-image generative models are a new and powerful way to generate visual artwork. However, the open-ended nature of text as interaction is double-edged; while users can input anything and have access to an infinite range of generations, they also must engage in brute-force trial and error with the text prompt when the result quality is poor. We conduct a study exploring what prompt keywords and model hyperparameters can help produce coherent outputs. In particular, we study prompts structured to include subject and style keywords and investigate success and failure modes of these prompts. Our evaluation of 5493 generations over the course of five experiments spans 51 abstract and concrete subjects as well as 51 abstract and figurative styles. From this evaluation, we present design guidelines that can help people produce better outcomes from text-to-image generative models.},
booktitle = {Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems},
articleno = {384},
numpages = {23},
keywords = {computational creativity, multimodal generative models, AI co-creation, prompt engineering., text-to-image, design guidelines},
location = {New Orleans, LA, USA},
series = {CHI '22}
}
% Dataset Generation
@misc{perez2022discovering,
title={Discovering Language Model Behaviors with Model-Written Evaluations},
author={Ethan Perez and Sam Ringer and Kamilė Lukošiūtė and Karina Nguyen and Edwin Chen and Scott Heiner and Craig Pettit and Catherine Olsson and Sandipan Kundu and Saurav Kadavath and Andy Jones and Anna Chen and Ben Mann and Brian Israel and Bryan Seethor and Cameron McKinnon and Christopher Olah and Da Yan and Daniela Amodei and Dario Amodei and Dawn Drain and Dustin Li and Eli Tran-Johnson and Guro Khundadze and Jackson Kernion and James Landis and Jamie Kerr and Jared Mueller and Jeeyoon Hyun and Joshua Landau and Kamal Ndousse and Landon Goldberg and Liane Lovitt and Martin Lucas and Michael Sellitto and Miranda Zhang and Neerav Kingsland and Nelson Elhage and Nicholas Joseph and Noemí Mercado and Nova DasSarma and Oliver Rausch and Robin Larson and Sam McCandlish and Scott Johnston and Shauna Kravec and Sheer El Showk and Tamera Lanham and Timothy Telleen-Lawton and Tom Brown and Tom Henighan and Tristan Hume and Yuntao Bai and Zac Hatfield-Dodds and Jack Clark and Samuel R. Bowman and Amanda Askell and Roger Grosse and Danny Hernandez and Deep Ganguli and Evan Hubinger and Nicholas Schiefer and Jared Kaplan},
year={2022},
eprint={2212.09251},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@misc{su2022selective,
title={Selective Annotation Makes Language Models Better Few-Shot Learners},
author={Hongjin Su and Jungo Kasai and Chen Henry Wu and Weijia Shi and Tianlu Wang and Jiayi Xin and Rui Zhang and Mari Ostendorf and Luke Zettlemoyer and Noah A. Smith and Tao Yu},
year={2022},
eprint={2209.01975},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
% applications
@misc{izacard2022atlas,
title={Atlas: Few-shot Learning with Retrieval Augmented Language Models},
author={Gautier Izacard and Patrick Lewis and Maria Lomeli and Lucas Hosseini and Fabio Petroni and Timo Schick and Jane Dwivedi-Yu and Armand Joulin and Sebastian Riedel and Edouard Grave},
year={2022},
eprint={2208.03299},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@misc{wang2022strudel,
title={STRUDEL: Structured Dialogue Summarization for Dialogue Comprehension},
author={Borui Wang and Chengcheng Feng and Arjun Nair and Madelyn Mao and Jai Desai and Asli Celikyilmaz and Haoran Li and Yashar Mehdad and Dragomir Radev},
year={2022},
eprint={2212.12652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
% Miscl
@misc{beurerkellner2022prompting,
title={Prompting Is Programming: A Query Language For Large Language Models},
author={Luca Beurer-Kellner and Marc Fischer and Martin Vechev},
year={2022},
eprint={2212.06094},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@misc{ratner2022parallel,
title={Parallel Context Windows Improve In-Context Learning of Large Language Models},
author={Nir Ratner and Yoav Levine and Yonatan Belinkov and Ori Ram and Omri Abend and Ehud Karpas and Amnon Shashua and Kevin Leyton-Brown and Yoav Shoham},
year={2022},
eprint={2212.10947},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@misc{bursztyn2022learning,
title={Learning to Perform Complex Tasks through Compositional Fine-Tuning of Language Models},
author={Victor S. Bursztyn and David Demeter and Doug Downey and Larry Birnbaum},
year={2022},
eprint={2210.12607},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@misc{wang2022supernaturalinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks},
author={Yizhong Wang and Swaroop Mishra and Pegah Alipoormolabashi and Yeganeh Kordi and Amirreza Mirzaei and Anjana Arunkumar and Arjun Ashok and Arut Selvan Dhanasekaran and Atharva Naik and David Stap and Eshaan Pathak and Giannis Karamanolakis and Haizhi Gary Lai and Ishan Purohit and Ishani Mondal and Jacob Anderson and Kirby Kuznia and Krima Doshi and Maitreya Patel and Kuntal Kumar Pal and Mehrad Moradshahi and Mihir Parmar and Mirali Purohit and Neeraj Varshney and Phani Rohitha Kaza and Pulkit Verma and Ravsehaj Singh Puri and Rushang Karia and Shailaja Keyur Sampat and Savan Doshi and Siddhartha Mishra and Sujan Reddy and Sumanta Patro and Tanay Dixit and Xudong Shen and Chitta Baral and Yejin Choi and Noah A. Smith and Hannaneh Hajishirzi and Daniel Khashabi},
year={2022},
eprint={2204.07705},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@article{gao2021making,
title={Making Pre-trained Language Models Better Few-shot Learners},
url={http://dx.doi.org/10.18653/v1/2021.acl-long.295},
DOI={10.18653/v1/2021.acl-long.295},
journal={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)},
publisher={Association for Computational Linguistics},
author={Gao, Tianyu and Fisch, Adam and Chen, Danqi},
year={2021} }
@misc{dang2022prompt,
title={How to Prompt? Opportunities and Challenges of Zero- and Few-Shot Learning for Human-AI Interaction in Creative Applications of Generative Models},
author={Hai Dang and Lukas Mecke and Florian Lehmann and Sven Goller and Daniel Buschek},
year={2022},
eprint={2209.01390},
archivePrefix={arXiv},
primaryClass={cs.HC}
}
@misc{akyrek2022measuring,
title={On Measuring Social Biases in Prompt-Based Multi-Task Learning},
author={Afra Feyza Akyürek and Sejin Paik and Muhammed Yusuf Kocyigit and Seda Akbiyik and Şerife Leman Runyun and Derry Wijaya},
year={2022},
eprint={2205.11605},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@misc{jin2022plot,
title={Plot Writing From Pre-Trained Language Models},
author={Yiping Jin and Vishakha Kadam and Dittaya Wanvarie},
year={2022},
eprint={2206.03021},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
% bias in llms
@inproceedings{nadeem-etal-2021-stereoset,
title = "{S}tereo{S}et: Measuring stereotypical bias in pretrained language models",
author = "Nadeem, Moin and
Bethke, Anna and
Reddy, Siva",
booktitle = "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)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.416",
doi = "10.18653/v1/2021.acl-long.416",
pages = "5356--5371",
abstract = "A stereotype is an over-generalized belief about a particular group of people, e.g., Asians are good at math or African Americans are athletic. Such beliefs (biases) are known to hurt target groups. Since pretrained language models are trained on large real-world data, they are known to capture stereotypical biases. It is important to quantify to what extent these biases are present in them. Although this is a rapidly growing area of research, existing literature lacks in two important aspects: 1) they mainly evaluate bias of pretrained language models on a small set of artificial sentences, even though these models are trained on natural data 2) current evaluations focus on measuring bias without considering the language modeling ability of a model, which could lead to misleading trust on a model even if it is a poor language model. We address both these problems. We present StereoSet, a large-scale natural English dataset to measure stereotypical biases in four domains: gender, profession, race, and religion. We contrast both stereotypical bias and language modeling ability of popular models like BERT, GPT-2, RoBERTa, and XLnet. We show that these models exhibit strong stereotypical biases. Our data and code are available at https://stereoset.mit.edu.",
}
% hallucinations in llms
@article{Ji_2022,
doi = {10.1145/3571730},
url = {https://doi.org/10.1145%2F3571730},
year = 2022,
month = {nov},
publisher = {Association for Computing Machinery ({ACM})},
author = {Ziwei Ji and Nayeon Lee and Rita Frieske and Tiezheng Yu and Dan Su and Yan Xu and Etsuko Ishii and Yejin Bang and Andrea Madotto and Pascale Fung},
title = {Survey of Hallucination in Natural Language Generation},
journal = {{ACM} Computing Surveys}
}
@inproceedings{yuan2022wordcraft,
title={Wordcraft: Story Writing With Large Language Models},
author={Yuan, Ann and Coenen, Andy and Reif, Emily and Ippolito, Daphne},
booktitle={27th International Conference on Intelligent User Interfaces},
pages={841--852},
year={2022}
}
@article{fadnavis2022pain,
title={PainPoints: A Framework for Language-based Detection of Chronic Pain and Expert-Collaborative Text-Summarization},
author={Fadnavis, Shreyas and Dhurandhar, Amit and Norel, Raquel and Reinen, Jenna M and Agurto, Carla and Secchettin, Erica and Schweiger, Vittorio and Perini, Giovanni and Cecchi, Guillermo},
journal={arXiv preprint arXiv:2209.09814},
year={2022}
}
@misc{wang2022selfinstruct,
title={Self-Instruct: Aligning Language Model with Self Generated Instructions},
author={Yizhong Wang and Yeganeh Kordi and Swaroop Mishra and Alisa Liu and Noah A. Smith and Daniel Khashabi and Hannaneh Hajishirzi},
year={2022},
eprint={2212.10560},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@misc{guo2022images,
title={From Images to Textual Prompts: Zero-shot VQA with Frozen Large Language Models},
author={Jiaxian Guo and Junnan Li and Dongxu Li and Anthony Meng Huat Tiong and Boyang Li and Dacheng Tao and Steven C. H. Hoi},
year={2022},
eprint={2212.10846},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
@misc{markov_2022,
title={New and improved content moderation tooling},
url={https://openai.com/blog/new-and-improved-content-moderation-tooling/},
journal={OpenAI},
publisher={OpenAI},
author={Markov, Todor},
year={2022},
month={Dec}
}
@misc{openai_api,
url={https://beta.openai.com/docs/guides/moderation},
year = {2022},
author={OpenAI}
}
@misc{openai_chatgpt,
url={https://openai.com/blog/chatgpt/},
year = {2022},
author={OpenAI}
}
% def of verbalizer
@misc{schick2020exploiting,
title={Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference},
author={Timo Schick and Hinrich Schütze},
year={2020},
eprint={2001.07676},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@article{lake2015human,
title={Human-level concept learning through probabilistic program induction},
author={Lake, Brenden M and Salakhutdinov, Ruslan and Tenenbaum, Joshua B},
journal={Science},
volume={350},
number={6266},
pages={1332--1338},
year={2015},
publisher={American Association for the Advancement of Science}
}
% music
@software{Forsgren_Martiros_2022,
author = {Forsgren, Seth* and Martiros, Hayk*},
title = {{Riffusion - Stable diffusion for real-time music generation}},
url = {https://riffusion.com/about},
year = {2022}
}
% writing
@article{bonta2022how,
url={https://www.streak.com/post/how-to-use-ai-to-write-perfect-cold-emails},
title = {How to use OpenAI’s ChatGPT to write the perfect cold email},
author = {Alice Bonta},
year = {2022},
month = {dec},
day = {7}
}
% cacti
@book{nobel2002cacti,
title={Cacti: biology and uses},
author={Nobel, Park S and others},
year={2002},
publisher={Univ of California Press}
}
% performance with misleading prompts
@article{webson2023itscomplicated,
title={Are Language Models Worse than Humans at Following Prompts? It’s Complicated},
author={Albert Webson and Alyssa Marie Loo and Qinan Yu and Ellie Pavlick},
journal={arXiv:2301.07085v1 [cs.CL]},
year={2023}
}
@misc{wang2023unleashing,
title={Unleashing Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration},
author={Zhenhailong Wang and Shaoguang Mao and Wenshan Wu and Tao Ge and Furu Wei and Heng Ji},
year={2023},
eprint={2307.05300},
archivePrefix={arXiv},
primaryClass={cs.AI}
}
% Prompt Injection
@misc{crothers2022machine,
title={Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods},
author={Evan Crothers and Nathalie Japkowicz and Herna Viktor},
year={2022},
eprint={2210.07321},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@misc{nin2023new,
title={New jailbreak based on virtual functions - smuggle illegal tokens to the backend.},
author={u/Nin_kat},
url={https://www.reddit.com/r/ChatGPT/comments/10urbdj/new_jailbreak_based_on_virtual_functions_smuggle},
year={2023},
}
@misc{kang2023exploiting,
title={Exploiting Programmatic Behavior of LLMs: Dual-Use Through Standard Security Attacks},
author={Daniel Kang and Xuechen Li and Ion Stoica and Carlos Guestrin and Matei Zaharia and Tatsunori Hashimoto},
year={2023},
eprint={2302.05733},
archivePrefix={arXiv},
primaryClass={cs.CR}
}
@misc{greshake2023youve,
title={More than you've asked for: A Comprehensive Analysis of Novel Prompt Injection Threats to Application-Integrated Large Language Models},
author={Kai Greshake and Sahar Abdelnabi and Shailesh Mishra and Christoph Endres and Thorsten Holz and Mario Fritz},
year={2023},
eprint={2302.12173},
archivePrefix={arXiv},
primaryClass={cs.CR}
}
@misc{kiho2023chatgpt,
title={ChatGPT "DAN" (and other "Jailbreaks")},
author={LEE KIHO},
url={https://github.com/0xk1h0/ChatGPT_DAN},
year={2023}
}
@misc{branch2022evaluating,
title={Evaluating the Susceptibility of Pre-Trained Language Models via Handcrafted Adversarial Examples},
author={Hezekiah J. Branch and Jonathan Rodriguez Cefalu and Jeremy McHugh and Leyla Hujer and Aditya Bahl and Daniel del Castillo Iglesias and Ron Heichman and Ramesh Darwishi},
year={2022},
eprint={2209.02128},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
% https://simonwillison.net/2022/Sep/12/prompt-injection/
@misc{simon2022inject,
title={Prompt injection attacks against GPT-3},
author={Simon Willison},
year={2022},
month={Sep},
url={https://simonwillison.net/2022/Sep/12/prompt-injection/}
}
@misc{goodside2022inject,
title={Exploiting GPT-3 prompts with malicious inputs that order the model to ignore its previous directions},
author={Riley Goodside},
year={2022},
url={https://twitter.com/goodside/status/1569128808308957185}
}
@misc{goodside2022history,
title={History Correction},
author={Riley Goodside},
year={2023},
url= {https://twitter.com/goodside/status/1610110111791325188?s=20&t=ulviQABPXFIIt4ZNZPAUCQ}
}
% prompt injection examples
@misc{chase2021adversarial,
title={adversarial-prompts},
author={Harrison Chase},
year={2022},
url={https://github.com/hwchase17/adversarial-prompts}
}
@misc{goodside2021gpt,
title={GPT-3 Prompt Injection Defenses},
author={Riley Goodside},
year={2022},
url={https://twitter.com/goodside/status/1578278974526222336?s=20&t=3UMZB7ntYhwAk3QLpKMAbw}
}
% post prompting defense
@misc{christoph2022talking,
author={Christoph Mark},
title={Talking to machines: prompt engineering & injection},
year={2022},
month={Oct},
day={3},
url={https://artifact-research.com/artificial-intelligence/talking-to-machines-prompt-engineering-injection/}
}
% other defenses, eliezer
@misc{armstrong2022using,
author={Stuart Armstrong, Rebecca Gorman},
title={Using GPT-Eliezer against ChatGPT Jailbreaking},
year={2022},
month={Dec},
day={6},
url={https://www.alignmentforum.org/posts/pNcFYZnPdXyL2RfgA/using-gpt-eliezer-against-chatgpt-jailbreaking}
}
% reasonable recs
@misc{selvi2022exploring,
author={Jose Selvi},
title={Exploring Prompt Injection Attacks},
year={2022},
month={Dec},
day={5},
url={https://research.nccgroup.com/2022/12/05/exploring-prompt-injection-attacks/}
}
% Prompt Leaking
@misc{kevinbing,
url={https://twitter.com/kliu128/status/1623472922374574080},
title = {The entire prompt of Microsoft Bing Chat?! (Hi, Sydney.)},
year = {2023},
author={Kevin Liu}
}
% Jailbreaking Sources
@misc{perez2022jailbreak,
doi = {10.48550/ARXIV.2211.09527},
url = {https://arxiv.org/abs/2211.09527},
author = {Perez, Fábio and Ribeiro, Ian},
keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Ignore Previous Prompt: Attack Techniques For Language Models},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
}
@misc{brundage_2022,
title={Lessons learned on Language Model Safety and misuse},
url={https://openai.com/blog/language-model-safety-and-misuse/},
journal={OpenAI},
publisher={OpenAI},
author={Brundage, Miles},
year={2022},
month={Mar}
}
@misc{wang2022jailbreak,
doi = {10.48550/ARXIV.2205.12390},
url = {https://arxiv.org/abs/2205.12390},
author = {Wang, Yau-Shian and Chang, Yingshan},
keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Toxicity Detection with Generative Prompt-based Inference},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
@misc{alice2022jailbreak,
url={https://twitter.com/alicemazzy/status/1598288519301976064},
title = {ok I saw a few people jailbreaking safeguards openai put on chatgpt so I had to give it a shot myself},
year = {2022},
author={Alice Maz}
}
@misc{miguel2022jailbreak,
url={https://twitter.com/m1guelpf/status/1598203861294252033},
title = {Bypass @OpenAI's ChatGPT alignment efforts with this one weird trick},
year = {2022},
author={Miguel Piedrafita}
}
@misc{derek2022jailbreak,
url={https://twitter.com/haus_cole/status/1598541468058390534},
title = {ChatGPT jailbreaking itself},
year = {2022},
author={Derek Parfait}
}
@misc{nero2022jailbreak,
url={https://twitter.com/NeroSoares/status/1608527467265904643},
title = {Using "pretend" on #ChatGPT can do some wild stuff. You can kind of get some insight on the future, alternative universe.},
year = {2022},
author={Nero Soares}
}
@misc{nick2022jailbreak,
url={https://twitter.com/NickEMoran/status/1598101579626057728},
title = {I kinda like this one even more!},
year = {2022},
author={Nick Moran}
}
@misc{sudo2022jailbreak,
url={https://www.sudo.ws/},
year = {2022},
author={Sudo}
}
@misc{sam2022jailbreak,
url={https://twitter.com/samczsun/status/1598679658488217601},
title = {uh oh},
year = {2022},
author={samczsun}
}
@misc{jonas2022jailbreak,
url={https://www.engraved.blog/building-a-virtual-machine-inside/},
title = {Building A Virtual Machine inside ChatGPT},
publisher={Engraved},
author = {Jonas Degrave},
year = {2022},
month = {Dec}
}
@misc{ignore_previous_prompt,
doi = {10.48550/ARXIV.2211.09527},
url = {https://arxiv.org/abs/2211.09527},
author = {Perez, Fábio and Ribeiro, Ian},
keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Ignore Previous Prompt: Attack Techniques For Language Models},
publisher = {arXiv},
year = {2022}
}
% Reliability
% mathprompter
@misc{imani2023mathprompter,
title={MathPrompter: Mathematical Reasoning using Large Language Models},
author={Shima Imani and Liang Du and Harsh Shrivastava},
year={2023},
eprint={2303.05398},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
% unreliable CoT
@misc{ye2022unreliability,
title={The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning},
author={Xi Ye and Greg Durrett},
year={2022},
eprint={2205.03401},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
% analyzing harm
@misc{si2022prompting,
title={Prompting GPT-3 To Be Reliable},
author={Chenglei Si and Zhe Gan and Zhengyuan Yang and Shuohang Wang and Jianfeng Wang and Jordan Boyd-Graber and Lijuan Wang},
year={2022},
eprint={2210.09150},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
% diverse prompts
@misc{li2022advance,
title={On the Advance of Making Language Models Better Reasoners},
author={Yifei Li and Zeqi Lin and Shizhuo Zhang and Qiang Fu and Bei Chen and Jian-Guang Lou and Weizhu Chen},
year={2022},
eprint={2206.02336},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
% Ask-Me-Anything Prompting
@misc{arora2022ama,
title = {Ask Me Anything: A simple strategy for prompting language models},
author = {Arora, Simran and Narayan, Avanika and Chen, Mayee F. and Orr, Laurel and Guha, Neel and Bhatia, Kush and Chami, Ines and Sala, Frederic and Ré, Christopher},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
year = {2022},
eprint={2210.02441},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
% problems with biases
@misc{zhao2021calibrate,
title={Calibrate Before Use: Improving Few-Shot Performance of Language Models},
author={Tony Z. Zhao and Eric Wallace and Shi Feng and Dan Klein and Sameer Singh},
year={2021},
eprint={2102.09690},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
% augment with search results
@misc{livin2022large,
title={Can large language models reason about medical questions?},
author={Valentin Liévin and Christoffer Egeberg Hother and Ole Winther},
year={2022},
eprint={2207.08143},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
% enhanced self consistency
@misc{mitchell2022enhancing,
title={Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inference},
author={Eric Mitchell and Joseph J. Noh and Siyan Li and William S. Armstrong and Ananth Agarwal and Patrick Liu and Chelsea Finn and Christopher D. Manning},
year={2022},
eprint={2211.11875},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
% bias in 0 shot CoT
@misc{shaikh2022second,
title={On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning},
author={Omar Shaikh and Hongxin Zhang and William Held and Michael Bernstein and Diyi Yang},
year={2022},
eprint={2212.08061},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@misc{chase2022evaluating,
title={Evaluating language models can be tricky},
author={Harrison Chase},
year={2022},
month={Dec},
day={26},
url={https://twitter.com/hwchase17/status/1607428141106008064}
}
% constitutional
@misc{bai2022constitutional,
title={Constitutional AI: Harmlessness from AI Feedback},
author={Yuntao Bai and Saurav Kadavath and Sandipan Kundu and Amanda Askell and Jackson Kernion and Andy Jones and Anna Chen and Anna Goldie and Azalia Mirhoseini and Cameron McKinnon and Carol Chen and Catherine Olsson and Christopher Olah and Danny Hernandez and Dawn Drain and Deep Ganguli and Dustin Li and Eli Tran-Johnson and Ethan Perez and Jamie Kerr and Jared Mueller and Jeffrey Ladish and Joshua Landau and Kamal Ndousse and Kamile Lukosuite and Liane Lovitt and Michael Sellitto and Nelson Elhage and Nicholas Schiefer and Noemi Mercado and Nova DasSarma and Robert Lasenby and Robin Larson and Sam Ringer and Scott Johnston and Shauna Kravec and Sheer El Showk and Stanislav Fort and Tamera Lanham and Timothy Telleen-Lawton and Tom Conerly and Tom Henighan and Tristan Hume and Samuel R. Bowman and Zac Hatfield-Dodds and Ben Mann and Dario Amodei and Nicholas Joseph and Sam McCandlish and Tom Brown and Jared Kaplan},
year={2022},
eprint={2212.08073},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
% Language Model Guides
@book{jurafsky2009,
author={Daniel Jurafsky and James H Martin},
editor={},
publisher={Prentice Hall},
title={Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition},
year={2009}
}
% Surveys
@article{liu2021pretrain,
title={Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing},
ISSN={1557-7341},
url={http://dx.doi.org/10.1145/3560815},
DOI={10.1145/3560815},
journal={ACM Computing Surveys},
publisher={Association for Computing Machinery (ACM)},
author={Liu, Pengfei and Yuan, Weizhe and Fu, Jinlan and Jiang, Zhengbao and Hayashi, Hiroaki and Neubig, Graham},
year={2022},
month={Sep}
}
@article{ning2022papers,
title={PromptPapers},
url={https://github.com/thunlp/PromptPapers},
year={2022},
month={Oct},
author={Ding, Ning and Hu, Shengding}
}
@misc{white2023prompt,
title={A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT},
author={Jules White and Quchen Fu and Sam Hays and Michael Sandborn and Carlos Olea and Henry Gilbert and Ashraf Elnashar and Jesse Spencer-Smith and Douglas C. Schmidt},
year={2023},
eprint={2302.11382},
archivePrefix={arXiv},
primaryClass={cs.SE}
}
% CoT
@misc{wei2022chain,
title={Chain of Thought Prompting Elicits Reasoning in Large Language Models},
author={Jason Wei and Xuezhi Wang and Dale Schuurmans and Maarten Bosma and Brian Ichter and Fei Xia and Ed Chi and Quoc Le and Denny Zhou},
year={2022},
eprint={2201.11903},