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Large language models (LLMs) have shown remarkable promise in simulating human language and behavior.
A coefficient of agreement for nominal scales
Jacob Cohen. 1960 · 1960
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Education and political tolerance: Testing the effects of cognitive sophistication and target group affect
Lawrence Bobo and Frederick C Licari. 1989 · 1989
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Bootstrap Methods: Another Look at the Jackknife , pages 569–593. Springer New York, New York, NY
Bradley Efron. 1992 · 1992
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Beyond the running tally: Partisan bias in political perceptions
Larry M. Bartels. 2002 · 2002
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Beyond beliefs: Religions bind individuals into moral communities
Jesse Graham and Jonathan Haidt. 2010 · 2010
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Subjective natural language problems: Motivations, applications, characterizations, and implications
Cecilia Ovesdotter Alm. 2011 · 2011
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The american national election studies 2012 time series study
ANES · 2012
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A general and simple method for obtaining r2 from generalized linear mixed-effects models
Shinichi Nakagawa and Holger Schielzeth. 2013 · 2013
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Addressing Age-Related Bias in Sentiment Analysis
Mark Diaz, Isaac Johnson, Amanda Lazar, Anne Marie Piper, and Darren Gergle. 2018 · 2018
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Personas and identity: Looking at multiple identities to inform the construction of personas
Nicola Marsden and Monika Pröbster. 2019 · 2019
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Social chemistry 101: Learning to reason about social and moral norms
Maxwell Forbes, Jena D. Hwang, Vered Shwartz, Maarten Sap, and Yejin Choi. 2020 · 2020
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The Pile: An 800GB Dataset of Diverse Text for Language Modeling
Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, Shawn Presser, and Connor Leahy. 2020 · 2020
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Social bias frames: Reasoning about social and power implications of language
Maarten Sap, Saadia Gabriel, Lianhui Qin, Dan Jurafsky, Noah A. Smith, and Yejin Choi. 2020 · 2020
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Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush. 2020 · 2020
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Documenting large webtext corpora: A case study on the colossal clean crawled corpus
Jesse Dodge, Maarten Sap, Ana Marasović, William Agnew, Gabriel Ilharco, Dirk Groeneveld, Margaret Mitchell, and Matt Gardner. 2021 · 2021
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Designing toxic content classification for a diversity of perspectives
Deepak Kumar, Patrick Gage Kelley, Sunny Consolvo, Joshua Mason, Elie Bursztein, Zakir Durumeric, Kurt Thomas, and Michael Bailey. 2021 · 2021
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performance: An R package for assessment, comparison and testing of statistical models
Daniel Lüdecke, Mattan S. Ben-Shachar, Indrajeet Patil, Philip Waggoner, and Dominique Makowski. 2021 · 2021
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Based on billions of words on the internet, people
April H. Bailey, Adina Williams, and Andrei Cimpian. 2022 · 2022
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Analyzing the effects of annotator gender across NLP tasks
Laura Biester, Vanita Sharma, Ashkan Kazemi, Naihao Deng, Steven R. Wilson, and Rada Mihalcea. 2022 · 2022
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Impact of Annotator Demographics on Sentiment Dataset Labeling
Yi Ding, Jacob You, Tonja-Katrin Machulla, Jennifer Jacobs, Pradeep Sen, and Tobias Höllerer. 2022 · 2022
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Jury Learning: Integrating Dissenting Voices into Machine Learning Models
Mitchell L. Gordon, Michelle S. Lam, Joon Sung Park, Kayur Patel, Jeff Hancock, Tatsunori Hashimoto, and Michael S. Bernstein. 2022 · 2022
Cited alongside, same era.
Dealing with disagreements: Looking beyond the majority vote in subjective annotations
Aida Mostafazadeh Davani, Mark Díaz, and Vinodkumar Prabhakaran. 2022 · 2022
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The “problem” of human label variation: On ground truth in data, modeling and evaluation
Barbara Plank. 2022 · 2022
Cited alongside, same era.
Annotators with attitudes: How annotator beliefs and identities bias toxic language detection
Maarten Sap, Swabha Swayamdipta, Laura Vianna, Xuhui Zhou, Yejin Choi, and Noah A. Smith. 2022 · 2022
Cited alongside, same era.
Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies
Gati V. Aher, Rosa I. Arriaga, and Adam Tauman Kalai. 2023 · 2023
Cited alongside, same era.
Camels in a changing climate: Enhancing lm adaptation with tulu 2
Hamish Ivison, Yizhong Wang, Valentina Pyatkin, Nathan Lambert, Matthew Peters, Pradeep Dasigi, Joel Jang, David Wadden, Noah A Smith, Iz Beltagy, et al. 2023 · 2023
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Junsol Kim and Byungkyu Lee. 2023 · 2023
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Large Language Models as Superpositions of Cultural Perspectives
Grgur Kovač, Masataka Sawayama, Rémy Portelas, Cédric Colas, Peter Ford Dominey, and Pierre-Yves Oudeyer. 2023 · 2023
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Crehate: Cross-cultural re-annotation of english hate speech dataset
Nayeon Lee, Chani Jung, Junho Myung, Jiho Jin, Juho Kim, and Alice Oh. 2023 · 2023
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MEGA: Multilingual evaluation of generative AI
Kabir Ahuja, Harshita Diddee, Rishav Hada, Millicent Ochieng, Krithika Ramesh, Prachi Jain, Akshay Nambi, Tanuja Ganu, Sameer Segal, Mohamed Ahmed, Kalika Bali, and Sunayana Sitaram. 2023 · 2023
Cited alongside, same era.
Out of One, Many: Using Language Models to Simulate Human Samples
Lisa P. Argyle, Ethan C. Busby, Nancy Fulda, Joshua R. Gubler, Christopher Rytting, and David Wingate. 2023 · 2023
Cited alongside, same era.
Toward a Perspectivist Turn in Ground Truthing for Predictive Computing
Federico Cabitza, Andrea Campagner, and Valerio Basile. 2023 · 2023
Cited alongside, same era.
CoMPosT: Characterizing and evaluating caricature in LLM simulations
Myra Cheng, Tiziano Piccardi, and Diyi Yang. 2023 · 2023
Cited alongside, same era.
Qlora: Efficient finetuning of quantized llms
Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer. 2023 · 2023
Cited alongside, same era.
Can ai language models replace human participants?
Danica Dillion, Niket Tandon, Yuling Gu, and Kurt Gray. 2023 · 2023
Cited alongside, same era.
Towards Measuring the Representation of Subjective Global Opinions in Language Models
Esin Durmus, Karina Nyugen, Thomas I. Liao, Nicholas Schiefer, Amanda Askell, Anton Bakhtin, Carol Chen, Zac Hatfield-Dodds, Danny Hernandez, Nicholas Joseph, Liane Lovitt, Sam McCandlish, Orowa Sikder, Alex Tamkin, Janel Thamkul, Jared Kaplan, Jack Clark, and Deep Ganguli. 2023 · 2023
Cited alongside, same era.
The ecological fallacy in annotation: Modeling human label variation goes beyond sociodemographics
Matthias Orlikowski, Paul Röttger, Philipp Cimiano, and Dirk Hovy. 2023 · 2023
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Generative Agents: Interactive Simulacra of Human Behavior
Joon Sung Park, Joseph C. O’Brien, Carrie J. Cai, Meredith Ringel Morris, Percy Liang, and Michael S. Bernstein. 2023 · 2023
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When do annotator demographics matter? measuring the influence of annotator demographics with the POPQUORN dataset
Jiaxin Pei and David Jurgens. 2023 · 2023
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Political polarization in the american public
Pew Research Center. 2014 · 2023
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Whose opinions do language models reflect?
Shibani Santurkar, Esin Durmus, Faisal Ladhak, Cinoo Lee, Percy Liang, and Tatsunori Hashimoto. 2023 · 2023
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NLPositionality: Characterizing design biases of datasets and models
Sebastin Santy, Jenny Liang, Ronan Le Bras, Katharina Reinecke, and Maarten Sap. 2023 · 2023
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Simulating social media using large language models to evaluate alternative news feed algorithms
Petter Törnberg, Diliara Valeeva, Justus Uitermark, and Christopher Bail. 2023 · 2023
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Llama 2: Open Foundation and Fine-Tuned Chat Models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez, Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushkar Mishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, and Thomas Scialom. 2023 · 2023
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The generative ai paradox:" what it can create, it may not understand"
Peter West, Ximing Lu, Nouha Dziri, Faeze Brahman, Linjie Li, Jena D Hwang, Liwei Jiang, Jillian Fisher, Abhilasha Ravichander, Khyathi Chandu, et al. 2023 · 2023
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Sensitivity, performance, robustness: Deconstructing the effect of sociodemographic prompting
Tilman Beck, Hendrik Schuff, Anne Lauscher, and Iryna Gurevych. 2024 · 2024
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Synthetic replacements for human survey data? the perils of large language models
James Bisbee, Joshua D. Clinton, Cassy Dorff, Brenton Kenkel, and Jennifer M. Larson. 2024 · 2024
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Toad: Task-oriented automatic dialogs with diverse response styles
Yinhong Liu, Yimai Fang, David Vandyke, and Nigel Collier. 2024 · 2024
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Diminished diversity-of-thought in a standard large language model
Peter S Park, Philipp Schoenegger, and Chongyang Zhu. 2024 · 2024
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Systematic biases in llm simulations of debates
Amir Taubenfeld, Yaniv Dover, Roi Reichart, and Ariel Goldstein. 2024 · 2024
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Large language models cannot replace human participants because they cannot portray identity groups
Angelina Wang, Jamie Morgenstern, and John P Dickerson. 2024 · 2024
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