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This study investigates the behaviors of Large Language Models (LLMs) when faced with conflicting prompts versus their internal memory.
A model of career decision making for college students
Vincent A Harren. 1979 · 1979
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Decision-making styles and problem-solving appraisal
Susan D Phillips, Nicholas J Pazienza, and Howard H Ferrin. 1984 · 1984
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Intriguing properties of neural networks
Joan Bruna, Christian Szegedy, Ilya Sutskever, Ian Goodfellow, Wojciech Zaremba, Rob Fergus, and Dumitru Erhan. 2014 · 2014
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Hossein Hosseini, Sreeram Kannan, Baosen Zhang, and Radha Poovendran. 2017 · 2017
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Know what you don’t know: Unanswerable questions for squad
Pranav Rajpurkar, Robin Jia, and Percy Liang. 2018 · 2018
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Autoprompt: Eliciting knowledge from language models with automatically generated prompts
Taylor Shin, Yasaman Razeghi, Robert L Logan IV, Eric Wallace, and Sameer Singh. 2020 · 2020
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Explanations for commonsenseqa: New dataset and models
Shourya Aggarwal, Divyanshu Mandowara, Vishwajeet Agrawal, Dinesh Khandelwal, Parag Singla, and Dinesh Garg. 2021 · 2021
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Making pre-trained language models better few-shot learners
Tianyu Gao, Adam Fisch, and Danqi Chen. 2021 · 2021
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Entity-based knowledge conflicts in question answering
Shayne Longpre, Kartik Perisetla, Anthony Chen, Nikhil Ramesh, Chris DuBois, and Sameer Singh. 2021 · 2021
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Webgpt: Browser-assisted question-answering with human feedback
Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, et al. 2021 · 2021
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Prompt programming for large language models: Beyond the few-shot paradigm
Laria Reynolds and Kyle McDonell. 2021 · 2021
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It’s not just size that matters: Small language models are also few-shot learners
Timo Schick and Hinrich Schütze. 2021 · 2021
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Calibrate before use: Improving few-shot performance of language models
Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh. 2021 · 2021
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Rich knowledge sources bring complex knowledge conflicts: Recalibrating models to reflect conflicting evidence
Hung-Ting Chen, Michael Zhang, and Eunsol Choi. 2022 · 2022
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e-care: a new dataset for exploring explainable causal reasoning
Li Du, Xiao Ding, Kai Xiong, Ting Liu, and Bing Qin. 2022 · 2022
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The inverse scaling prize
Ian McKenzie, Alexander Lyzhov, Alicia Parrish, Ameya Prabhu, Aaron Mueller, Najoung Kim, Sam Bowman, and Ethan Perez. 2022 · 2022
Cited alongside, same era.
Cross-task generalization via natural language crowdsourcing instructions
Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2023 · 2023
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Co-writing screenplays and theatre scripts with language models: Evaluation by industry professionals
Piotr Mirowski, Kory W Mathewson, Jaylen Pittman, and Richard Evans. 2023 · 2023
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Openai: Gpt-4
OpenAI. 2023 · 2023
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Large language models can be easily distracted by irrelevant context
Freda Shi, Xinyun Chen, Kanishka Misra, Nathan Scales, David Dohan, Ed Chi, Nathanael Schärli, and Denny Zhou. 2023 · 2023
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Miles Turpin, Julian Michael, Ethan Perez, and Samuel R Bowman. 2023 · 2023
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Swaroop Mishra, Daniel Khashabi, Chitta Baral, and Hannaneh Hajishirzi. 2022 · 2022
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Prompting gpt-3 to be reliable
Chenglei Si, Zhe Gan, Zhengyuan Yang, Shuohang Wang, Jianfeng Wang, Jordan Boyd-Graber, and Lijuan Wang. 2022 · 2022
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Musique: Multihop questions via single-hop question composition
Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal. 2022 · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed H Chi, Quoc V Le, Denny Zhou, et al. 2022 · 2022
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Anthropic: Claude
Anthropic. 2023 · 2023
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Google: Bard
Google. 2023 · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023a
Cited in the paper.
On the robustness of chatgpt: An adversarial and out-of-distribution perspective
Jindong Wang, Xixu Hu, Wenxin Hou, Hao Chen, Runkai Zheng, Yidong Wang, Linyi Yang, Haojun Huang, Wei Ye, Xiubo Geng, et al. 2023 · 2023
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Jian Xie, Kai Zhang, Jiangjie Chen, Renze Lou, and Yu Su. 2023 · 2023
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Progressive-hint prompting improves reasoning in large language models
Chuanyang Zheng, Zhengying Liu, Enze Xie, Zhenguo Li, and Yu Li. 2023 · 2023
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Promptbench: Towards evaluating the robustness of large language models on adversarial prompts
Kaijie Zhu, Jindong Wang, Jiaheng Zhou, Zichen Wang, Hao Chen, Yidong Wang, Linyi Yang, Wei Ye, Neil Zhenqiang Gong, Yue Zhang, et al. 2023 · 2023
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On robustness of prompt-based semantic parsing with large pre-trained language model: An empirical study on codex
Terry Yue Zhuo, Zhuang Li, Yujin Huang, Fatemeh Shiri, Weiqing Wang, Gholamreza Haffari, and Yuan-Fang Li. 2023 · 2023
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Dr chatgpt, tell me what i want to hear: How prompt knowledge impacts health answer correctness
Guido Zuccon and Bevan Koopman. 2023 · 2023
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