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Large language models (LLMs) are remarkable data annotators.
Utility data annotation with amazon mechanical turk
Alexander Sorokin and David Forsyth. 2008 · 2008
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009 · 2009
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Demographics of mechanical turk
Panagiotis G Ipeirotis. 2010 · 2010
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Using the amazon mechanical turk for transcription of spoken language
Matthew Marge, Satanjeev Banerjee, and Alexander I Rudnicky. 2010 · 2010
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Amazon’s mechanical turk: A new source of inexpensive, yet high-quality, data?
Michael Buhrmester, Tracy Kwang, and Samuel D Gosling. 2011 · 2011
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Crowdsourcing multi-label classification for taxonomy creation
Jonathan Bragg, Daniel Weld, et al. 2013 · 2013
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A multi-group analysis of online survey respondent data quality: Comparing a regular usa consumer panel to mturk samples
Scott M Smith, Catherine A Roster, Linda L Golden, and Gerald S Albaum. 2016 · 2016
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Who is mturk? personal characteristics and sample consistency of these online workers
Martin J Burnham, Yen K Le, and Ralph L Piedmont. 2018 · 2018
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Ghost work: How to stop Silicon Valley from building a new global underclass
Mary L Gray and Siddharth Suri. 2019 · 2019
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Message distortion in information cascades
Manoel Horta Ribeiro, Kristina Gligoric, and Robert West. 2019 · 2019
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Who are the turkers? a characterization of mturk workers using the personality assessment inventory
Morgan N McCredie and Leslie C Morey. 2019 · 2019
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Bit by bit: Social research in the digital age
Matthew J Salganik. 2019 · 2019
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Untold history of ai: How amazon’s mechanical turkers got squeezed inside the machine
Oscar Schwartz. 2019 · 2019
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An mturk crisis? shifts in data quality and the impact on study results
Michael Chmielewski and Sarah C Kucker. 2020 · 2020
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The shape of and solutions to the mturk quality crisis
Ryan Kennedy, Scott Clifford, Tyler Burleigh, Philip D Waggoner, Ryan Jewell, and Nicholas JG Winter. 2020 · 2020
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Gpt-3: What’s it good for?
Robert Dale. 2021 · 2021
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Fingerprinting fine-tuned language models in the wild
Nirav Diwan, Tanmoy Chakravorty, and Zubair Shafiq. 2021 · 2021
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A checklist to combat cognitive biases in crowdsourcing
Tim Draws, Alisa Rieger, Oana Inel, Ujwal Gadiraju, and Nava Tintarev. 2021 · 2021
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How do older adults recruited using mturk differ from those in a national probability sample?
Aaron M Ogletree and Benjamin Katz. 2021 · 2021
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Data and its (dis) contents: A survey of dataset development and use in machine learning research
Amandalynne Paullada, Inioluwa Deborah Raji, Emily M Bender, Emily Denton, and Alex Hanna. 2021 · 2021
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Chatgpt: Jack of all trades, master of none
Jan Kocoń, Igor Cichecki, Oliwier Kaszyca, Mateusz Kochanek, Dominika Szydło, Joanna Baran, Julita Bielaniewicz, Marcin Gruza, Arkadiusz Janz, Kamil Kanclerz, et al. 2023 · 2023
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Chatgpt as a factual inconsistency evaluator for text summarization
Zheheng Luo, Qianqian Xie, and Sophia Ananiadou. 2023 · 2023
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OpenAI. 2023 · 2023
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On the risk of misinformation pollution with large language models
Yikang Pan, Liangming Pan, Wenhu Chen, Preslav Nakov, Min-Yen Kan, and William Yang Wang. 2023 · 2023
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War of the chatbots: Bard, bing chat, chatgpt, ernie and beyond. the new ai gold rush and its impact on higher education
Jürgen Rudolph, Shannon Tan, and Samson Tan. 2023 · 2023
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Gordon Pennycook, Ziv Epstein, Mohsen Mosleh, Antonio A Arechar, Dean Eckles, and David G Rand. 2021 · 2021
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Out of one, many: Using language models to simulate human samples
Lisa P Argyle, Ethan C Busby, Nancy Fulda, Joshua Gubler, Christopher Rytting, and David Wingate. 2022 · 2022
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Wanli: Worker and ai collaboration for natural language inference dataset creation
Alisa Liu, Swabha Swayamdipta, Noah A Smith, and Yejin Choi. 2022 · 2022
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Text embeddings by weakly-supervised contrastive pre-training
Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, and Furu Wei. 2022 · 2022
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A survey of controllable text generation using transformer-based pre-trained language models
Hanqing Zhang, Haolin Song, Shaoyu Li, Ming Zhou, and Dawei Song. 2022 · 2022
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Can ai language models replace human participants?
Danica Dillion, Niket Tandon, Yuling Gu, and Kurt Gray. 2023 · 2023
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Chatgpt outperforms crowd-workers for text-annotation tasks
Fabrizio Gilardi, Meysam Alizadeh, and Maël Kubli. 2023 · 2023
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Can ai-generated text be reliably detected?
Vinu Sankar Sadasivan, Aounon Kumar, Sriram Balasubramanian, Wenxiao Wang, and Soheil Feizi. 2023 · 2023
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Whose opinions do language models reflect?
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The curse of recursion: Training on generated data makes models forget
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string2string: A modern python library for string-to-string algorithms
Mirac Suzgun, Stuart M Shieber, and Dan Jurafsky. 2023 · 2023
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Petter Törnberg. 2023 · 2023
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Ghostbuster: Detecting text ghostwritten by large language models
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Is chatgpt a good nlg evaluator? a preliminary study
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Cheat: A large-scale dataset for detecting chatgpt-written abstracts
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Can large language models transform computational social science?
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