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Large language models (LLMs) are incredibly powerful at comprehending and generating data in the form of text, but are brittle and error-prone.
Multiple Imputation for Nonresponse in Surveys
D. B. Rubin · 1987
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Crowdsourcing user studies with mechanical turk
A. Kittur, E. H. Chi, and B. Suh · 2008
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Cheap and fast - but is it good? evaluating non-expert annotations for natural language tasks
R. Snow, B. O’Connor, D. Jurafsky, and A. Y. Ng · 2008
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The role of game theory in human computation systems
S. Jain and D. C. Parkes · 2009
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Soylent: a word processor with a crowd inside
M. S. Bernstein, G. Little, R. C. Miller, B. Hartmann, M. S. Ackerman, D. R. Karger, D. Crowell, and K. Panovich · 2010
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Quality management on amazon mechanical turk
P. G. Ipeirotis, F. Provost, and J. Wang · 2010
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Evaluating and improving the usability of mechanical turk for low-income workers in india
S. Khanna, A. Ratan, J. Davis, and W. Thies · 2010
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Evaluation of entity resolution approaches on real-world match problems
H. Köpcke, A. Thor, and E. Rahm · 2010
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Turkit: human computation algorithms on mechanical turk
G. Little, L. B. Chilton, M. Goldman, and R. C. Miller · 2010
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Opportunities for crowdsourcing research on amazon mechanical turk
J. J. Chen, N. J. Menezes, A. D. Bradley, and T. North · 2011
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Crowddb: answering queries with crowdsourcing
M. J. Franklin, D. Kossmann, T. Kraska, S. Ramesh, and R. Xin · 2011
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Human-powered sorts and joins
A. Marcus, E. Wu, D. R. Karger, S. Madden, and R. C. Miller · 2011
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Crowdsourced databases: Query processing with people
A. Marcus, E. Wu, S. Madden, and R. C. Miller · 2011
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So who won?: dynamic max discovery with the crowd
S. Guo, A. G. Parameswaran, and H. Garcia-Molina · 2012
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Collaboratively crowdsourcing workflows with turkomatic
A. P. Kulkarni, M. Can, and B. Hartmann · 2012
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Counting with the crowd
A. Marcus, D. R. Karger, S. Madden, R. Miller, and S. Oh · 2012
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Crowdscreen: algorithms for filtering data with humans
A. G. Parameswaran, H. Garcia-Molina, H. Park, N. Polyzotis, A. Ramesh, and J. Widom · 2012
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Deco: declarative crowdsourcing
A. G. Parameswaran, H. Park, H. Garcia-Molina, N. Polyzotis, and J. Widom · 2012
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CrowdER: Crowdsourcing Entity Resolution
J. Wang, T. Kraska, M. J. Franklin, and J. Feng · 2012
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Leveraging transitive relations for crowdsourced joins
J. Wang, G. Li, T. Kraska, M. J. Franklin, and J. Feng · 2013
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SIGMOD 2014
C. Gokhale, S. Das, A. Doan, J. F. Naughton, N. Rampalli, J. Shavlik, and X. Zhu · 2014
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Crowdsourced data management: Industry and academic perspectives
A. Marcus, A. Parameswaran, et al · 2015
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Debiasing crowdsourced batches
H. Zhuang, A. G. Parameswaran, D. Roth, and J. Han · 2015
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It’s just a matter of perspective (s): Crowd-powered consensus organization of corpora
A. Jain, J. Y. Seo, K. Goel, A. Kuznetsov, A. Parameswaran, and H. Sundaram · 2016
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Crowdsourced data management: A survey
G. Li, J. Wang, Y. Zheng, and M. J. Franklin · 2016
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Dynamic Strategies for Crowdsourced Data Management
A. R. Khan · 2017
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Data validation for machine learning
E. Breck, N. Polyzotis, S. Roy, S. Whang, and M. Zinkevich · 2019
Cited alongside, same era.
The curious case of neural text degeneration
A. Holtzman, J. Buys, L. Du, M. Forbes, and Y. Choi · 2019
Cited alongside, same era.
Language models are few-shot learners
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al · 2020
Cited alongside, same era.
Autoprompt: Eliciting knowledge from language models with automatically generated prompts
Ai chains: Transparent and controllable human-ai interaction by chaining large language model prompts
T. Wu, M. Terry, and C. J. Cai · 2022
Later among the works it cites.
Large language models are human-level prompt engineers
Y. Zhou, A. I. Muresanu, Z. Han, K. Paster, S. Pitis, H. Chan, and J. Ba · 2022
Later among the works it cites.
https://github.com/openai/openai-cookbook, 2023
Openai cookbook · 2023
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Let’s sample step by step: Adaptive-consistency for efficient reasoning with llms
P. Aggarwal, A. Madaan, Y. Yang, et al · 2023
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Out of one, many: Using language models to simulate human samples
L. P. Argyle, E. C. Busby, N. Fulda, J. R. Gubler, C. Rytting, and D. Wingate · 2023
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T. Shin, Y. Razeghi, R. L. Logan IV, E. Wallace, and S. Singh · 2020
Cited alongside, same era.
Openprompt: An open-source framework for prompt-learning
N. Ding, S. Hu, W. Zhao, Y. Chen, Z. Liu, H.-T. Zheng, and M. Sun · 2021
Cited alongside, same era.
What makes good in-context examples for gpt-
J. Liu, D. Shen, Y. Zhang, B. Dolan, L. Carin, and W. Chen · 2021
Cited alongside, same era.
Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity
Y. Lu, M. Bartolo, A. Moore, S. Riedel, and P. Stenetorp · 2021
Cited alongside, same era.
Capturing semantics for imputation with pre-trained language models
Y. Mei, S. Song, C. Fang, H. Yang, J. Fang, and J. Long · 2021
Cited alongside, same era.
Calibrate before use: Improving few-shot performance of language models
Z. Zhao, E. Wallace, S. Feng, D. Klein, and S. Singh · 2021
Cited alongside, same era.
Language models as agent models
J. Andreas · 2022
Cited alongside, same era.
S. Bubeck, V. Chandrasekaran, R. Eldan, J. Gehrke, E. Horvitz, E. Kamar, P. Lee, Y. T. Lee, Y. Li, S. Lundberg, et al · 2023
Closest in time.
https://www.wsj.com/articles/chatgpt-ask-the-right-question-12d0f035, 2023
ChatGPT Can Give Great Answers. But Only If You Know How to Ask the Right Question, Wall Street Journal · 2023
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Frugalgpt: How to use large language models while reducing cost and improving performance
L. Chen, M. Zaharia, and J. Zou · 2023
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Chatgpt outperforms crowd-workers for text-annotation tasks
F. Gilardi, M. Alizadeh, and M. Kubli · 2023
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Lost in the middle: How language models use long contexts, 2023
N. F. Liu, K. Lin, J. Hewitt, A. Paranjape, M. Bevilacqua, F. Petroni, and P. Liang · 2023
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Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
P. Liu, W. Yuan, J. Fu, Z. Jiang, H. Hayashi, and G. Neubig · 2023
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Self-refine: Iterative refinement with self-feedback
A. Madaan, N. Tandon, P. Gupta, S. Hallinan, L. Gao, S. Wiegreffe, U. Alon, N. Dziri, S. Prabhumoye, Y. Yang, et al · 2023
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Mom, Dad, I Want To Be A Prompt Engineer, Forbes Magazine, https://www.forbes.com/sites/craigsmith/2023/04/05/mom-dad-i-want-to-be-a-prompt-engineer/, 2023
2023
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Whose opinions do language models reflect?
S. Santurkar, E. Durmus, F. Ladhak, C. Lee, P. Liang, and T. Hashimoto · 2023
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P. Törnberg · 2023
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V. Veselovsky, M. H. Ribeiro, and R. West · 2023
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Are language models worse than humans at following prompts? it’s complicated
A. Webson, A. M. Loo, Q. Yu, and E. Pavlick · 2023
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Prompt engineering
L. Weng · 2023
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A prompt pattern catalog to enhance prompt engineering with chatgpt
J. White, Q. Fu, S. Hays, M. Sandborn, C. Olea, H. Gilbert, A. Elnashar, J. Spencer-Smith, and D. C. Schmidt · 2023
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Wikidot, Logprobs, http://gptprompts.wikidot.com/intro:logprobs, 2023
2023
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The problem with langchain
M. Woolf · 2023
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Llms as workers in human-computational algorithms? replicating crowdsourcing pipelines with llms
T. Wu, H. Zhu, M. Albayrak, A. Axon, A. Bertsch, W. Deng, Z. Ding, B. Guo, S. Gururaja, T.-S. Kuo, et al · 2023
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Why johnny can’t prompt: how non-ai experts try (and fail) to design llm prompts
J. Zamfirescu-Pereira, R. Y. Wong, B. Hartmann, and Q. Yang · 2023
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