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Detecting biases in the outputs produced by generative models is essential to reduce the potential risks associated with their application in critical settings.
A very brief measure of the Big-Five personality domains
Samuel D Gosling, Peter J Rentfrow, and William B Swann Jr. 2003 · 2003
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John Robinson. 2014 · 2014
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Recursive patterns in online echo chambers
Emanuele Brugnoli, Matteo Cinelli, Walter Quattrociocchi, and Antonio Scala. 2019 · 2019
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Reducing sentiment bias in language models via counterfactual evaluation
Po-Sen Huang, Huan Zhang, Ray Jiang, Robert Stanforth, Johannes Welbl, Jack Rae, Vishal Maini, Dani Yogatama, and Pushmeet Kohli. 2019 · 2019
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On the dangers of stochastic parrots: Can language models be too big?. In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency . 610–623
Emily M Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021 · 2021
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Bold: Dataset and metrics for measuring biases in open-ended language generation. In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency . 862–872
Jwala Dhamala, Tony Sun, Varun Kumar, Satyapriya Krishna, Yada Pruksachatkun, Kai-Wei Chang, and Rahul Gupta. 2021 · 2021
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Societal biases in language generation: Progress and challenges
Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, and Nanyun Peng. 2021 · 2021
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Measuring fairness with biased rulers: A comparative study on bias metrics for pre-trained language models. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics . Association for Computational Linguistics, 1693–1706
Pieter Delobelle, Ewoenam Kwaku Tokpo, Toon Calders, and Bettina Berendt. 2022 · 2022
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https://www.pewresearch.org/social-trends/2022/06/28/americans-complex-views-on-gender-identity-and-transgender-issues/
PewResearchCenter. 2022 · 2022
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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
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Yihan Cao, Siyu Li, Yixin Liu, Zhiling Yan, Yutong Dai, Philip S Yu, and Lichao Sun. 2023 · 2023
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Probing explicit and implicit gender bias through LLM conditional text generation. arXiv
X Dong, Y Wang, PS Yu, and J Caverlee. 2023 · 2023
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https://news.gallup.com/poll/101884/how-should-cite-gallup-my-work.aspx
GallupPoll. 2023 · 2023
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Bias runs deep: Implicit reasoning biases in persona-assigned llms
Shashank Gupta, Vaishnavi Shrivastava, Ameet Deshpande, Ashwin Kalyan, Peter Clark, Ashish Sabharwal, and Tushar Khot. 2023 · 2023
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Gender bias and stereotypes in large language models. In Proceedings of the ACM collective intelligence conference . 12–24
Hadas Kotek, Rikker Dockum, and David Sun. 2023 · 2023
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Social learning: Towards collaborative learning with large language models
Amirkeivan Mohtashami, Florian Hartmann, Sian Gooding, Lukas Zilka, Matt Sharifi, et al · 2023
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Generative agents: Interactive simulacra of human behavior. In Proceedings of the 36th annual acm symposium on user interface software and technology . 1–22
Joon Sung Park, Joseph O’Brien, Carrie Jun Cai, Meredith Ringel Morris, Percy Liang, and Michael S Bernstein. 2023 · 2023
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Ethical reasoning over moral alignment: A case and framework for in-context ethical policies in LLMs
Abhinav Rao, Aditi Khandelwal, Kumar Tanmay, Utkarsh Agarwal, and Monojit Choudhury. 2023 · 2023
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Personality traits in large language models
Mustafa Safdari, Greg Serapio-García, Clément Crepy, Stephen Fitz, Peter Romero, Luning Sun, Marwa Abdulhai, Aleksandra Faust, and Maja Matarić. 2023 · 2023
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Large Language Models as Subpopulation Representative Models: A Review
Gabriel Simmons and Christopher Hare. 2023 · 2023
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Multi-agent collaboration: Harnessing the power of intelligent llm agents
Yashar Talebirad and Amirhossein Nadiri. 2023 · 2023
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”Kelly is a warm person, Joseph is a role model”: Gender biases in llm-generated reference letters
Yixin Wan, George Pu, Jiao Sun, Aparna Garimella, Kai-Wei Chang, and Nanyun Peng. 2023 · 2023
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Autogen: Enabling next-gen llm applications via multi-agent conversation framework
Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Shaokun Zhang, Erkang Zhu, Beibin Li, Li Jiang, Xiaoyun Zhang, and Chi Wang. 2023 · 2023
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LangChain
Harrison, Chase. 2024 · 2024
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Quantifying the persona effect in llm simulations
Tiancheng Hu and Nigel Collier. 2024 · 2024
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MoralBench: Moral Evaluation of LLMs
Jianchao Ji, Yutong Chen, Mingyu Jin, Wujiang Xu, Wenyue Hua, and Yongfeng Zhang. 2024 · 2024
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Lucio La Cava and Andrea Tagarelli. 2024 · 2024
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A survey on LLM-based multi-agent systems: workflow, infrastructure, and challenges
Xinyi Li, Sai Wang, Siqi Zeng, Yu Wu, and Yi Yang. 2024 · 2024
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Kai-Cheng Yang and Filippo Menczer. 2023 · 2023
Cited alongside, same era.
Can Large Language Models Transform Computational Social Science?
Caleb Ziems, William B. Held, Omar Shaikh, Jiaao Chen, Zhehao Zhang, and Diyi Yang. 2023 · 2023
Cited alongside, same era.
Ariel Flint Ashery, Luca Maria Aiello, and Andrea Baronchelli. 2024 · 2024
Cited alongside, same era.
Can Generative AI improve social science?
Christopher A Bail. 2024 · 2024
Cited alongside, same era.
Measuring Political Bias in Large Language Models: What Is Said and How It Is Said
Yejin Bang, Delong Chen, Nayeon Lee, and Pascale Fung. 2024 · 2024
Cited alongside, same era.
Towards Implicit Bias Detection and Mitigation in Multi-Agent LLM Interactions
Angana Borah and Rada Mihalcea. 2024 · 2024
Cited alongside, same era.
The persuasive power of large language models. In Proceedings of the International AAAI Conference on Web and Social Media , Vol. 18. 152–163
Simon Martin Breum, Daniel Vædele Egdal, Victor Gram Mortensen, Anders Giovanni Møller, and Luca Maria Aiello. 2024 · 2024
Cited alongside, same era.
Humans or llms as the judge? a study on judgement biases
Guiming Hardy Chen, Shunian Chen, Ziche Liu, Feng Jiang, and Benyou Wang. 2024 · 2024
Cited alongside, same era.
Echo Chambers in Online Social Networks: A Systematic Literature Review
Amin Mahmoudi, Dariusz Jemielniak, and Leon Ciechanowski. 2024 · 2024
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https://www.pewresearch.org/politics/2024/06/06/racial-attitudes-and-the-2024-election/
PewResearchCenter. 2024 · 2024
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Whose Side Are You On? Investigating the Political Stance of Large Language Models
Pagnarasmey Pit, Xingjun Ma, Mike Conway, Qingyu Chen, James Bailey, Henry Pit, Putrasmey Keo, Watey Diep, and Yu-Gang Jiang. 2024 · 2024
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Y Social: an LLM-powered Social Media Digital Twin
Giulio Rossetti, Massimo Stella, Rémy Cazabet, Katherine Abramski, Erica Cau, Salvatore Citraro, Andrea Failla, Riccardo Improta, Virginia Morini, and Valentina Pansanella. 2024 · 2024
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The Problems of LLM-generated Data in Social Science Research
Luca Rossi, Katherine Harrison, and Irina Shklovski. 2024 · 2024
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The political preferences of LLMs
David Rozado. 2024 · 2024
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Challenging Fairness: A Comprehensive Exploration of Bias in LLM-Based Recommendations
Shahnewaz Karim Sakib and Anindya Bijoy Das. 2024 · 2024
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In-context impersonation reveals Large Language Models’ strengths and biases
Leonard Salewski, Stephan Alaniz, Isabel Rio-Torto, Eric Schulz, and Zeynep Akata. 2024 · 2024
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Evaluating the moral beliefs encoded in llms
Nino Scherrer, Claudia Shi, Amir Feder, and David Blei. 2024 · 2024
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Ask LLMs Directly,” What shapes your bias?”: Measuring Social Bias in Large Language Models
Jisu Shin, Hoyun Song, Huije Lee, Soyeong Jeong, and Jong C Park. 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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The Power of LLM-Generated Synthetic Data for Stance Detection in Online Political Discussions
Stefan Sylvius Wagner, Maike Behrendt, Marc Ziegele, and Stefan Harmeling. 2024 · 2024
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Assessing Social Alignment: Do Personality-Prompted Large Language Models Behave Like Humans?. In NeurIPS 2024 Workshop on Behavioral Machine Learning
Ivan Zakazov, Mikolaj Boronski, Lorenzo Drudi, and Robert West. [n. d.] · 2024
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