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The integration of Large Language Models (LLMs) in information retrieval has raised a critical reevaluation of fairness in the text-ranking models.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 1901
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Multi-stage document ranking with BERT
Rodrigo Frassetto Nogueira, Wei Yang, Kyunghyun Cho, and Jimmy Lin. 2019 · 1910
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CrowS-pairs: A challenge dataset for measuring social biases in masked language models
Nikita Nangia, Clara Vania, Rasika Bhalerao, and Samuel R. Bowman. 2020 · 1967
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Measuring fairness in ranked outputs
Ke Yang and Julia Stoyanovich. 2017 · 2017
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Fa*ir: A fair top-k ranking algorithm
Meike Zehlike, Francesco Bonchi, Carlos Castillo, Sara Hajian, Mohamed Megahed, and Ricardo Baeza-Yates. 2017 · 2017
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Equity of attention: Amortizing individual fairness in rankings
Asia J. Biega, Krishna P. Gummadi, and Gerhard Weikum. 2018 · 2018
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Ranking with fairness constraints
L. Elisa Celis, Damian Straszak, and Nisheeth K. Vishnoi. 2018 · 2018
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Selection Problems in the Presence of Implicit Bias
Jon Kleinberg and Manish Raghavan. 2018 · 2018
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Fairness of exposure in rankings
Ashudeep Singh and Thorsten Joachims. 2018 · 2018
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Online set selection with fairness and diversity constraints
Julia Stoyanovich, Ke Yang, and HV Jagadish. 2018 · 2018
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Designing fair ranking schemes
Abolfazl Asudeh, H. V. Jagadish, Julia Stoyanovich, and Gautam Das. 2019 · 2019
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Fairness in recommendation ranking through pairwise comparisons
Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Li Wei, Yi Wu, Lukasz Heldt, Zhe Zhao, Lichan Hong, Ed H. Chi, and Cristos Goodrow. 2019 · 2019
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ifair: Learning individually fair data representations for algorithmic decision making
Preethi Lahoti, Gerhard Weikum, and Krishna P. Gummadi. 2019 · 2019
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Balanced ranking with diversity constraints
Ke Yang, Vasilis Gkatzelis, and Julia Stoyanovich. 2019 · 2019
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Realtoxicityprompts: Evaluating neural toxic degeneration in language models
Samuel Gehman, Suchin Gururangan, Maarten Sap, Yejin Choi, and Noah A. Smith. 2020 · 2020
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Social biases in NLP models as barriers for persons with disabilities
Ben Hutchinson, Vinodkumar Prabhakaran, Emily Denton, Kellie Webster, Yu Zhong, and Stephen Denuyl. 2020 · 2020
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Rodrigo Nogueira and Kyunghyun Cho. 2020 · 2020
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Document ranking with a pretrained sequence-to-sequence model
Rodrigo Nogueira, Zhiying Jiang, Ronak Pradeep, and Jimmy Lin. 2020 · 2020
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Reducing disparate exposure in ranking: A learning to rank approach
Meike Zehlike and Carlos Castillo. 2020 · 2020
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Matching code and law: Achieving algorithmic fairness with optimal transport
Meike Zehlike, Philipp Hacker, and Emil Wiedemann. 2020 · 2020
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Persistent anti-muslim bias in large language models
Abubakar Abid, Maheen Farooqi, and James Zou. 2021 · 2021
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Fairnas: Rethinking evaluation fairness of weight sharing neural architecture search
Xiangxiang Chu, Bo Zhang, and Ruijun Xu. 2021 · 2021
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Overview of the trec 2021 fair ranking track
Michael D. Ekstrand, Graham McDonald, Amifa Raj, and Isaac Johnson. 2022 · 2021
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Text-to-text multi-view learning for passage re-ranking
Jia-Huei Ju, Jheng-Hong Yang, and Chuan-Ju Wang. 2021 · 2021
Sparks of artificial general intelligence: Early experiments with GPT-4
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott M. Lundberg, Harsha Nori, Hamid Palangi, Marco Túlio Ribeiro, and Yi Zhang. 2023 · 2023
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Marked personas: Using natural language prompts to measure stereotypes in language models
Myra Cheng, Esin Durmus, and Dan Jurafsky. 2023 · 2023
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Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed. 2023 · 2023
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Gender bias and stereotypes in large language models
Hadas Kotek, Rikker Dockum, and David Sun. 2023 · 2023
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The expando-mono-duo design pattern for text ranking with pretrained sequence-to-sequence models
Ronak Pradeep, Rodrigo Frassetto Nogueira, and Jimmy Lin. 2021 · 2021
Cited alongside, same era.
Constitutional ai: Harmlessness from ai feedback
Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, Carol Chen, Catherine Olsson, Christopher Olah, Danny Hernandez, Dawn Drain, Deep Ganguli, Dustin Li, Eli Tran-Johnson, Ethan Perez, Jamie Kerr, Jared Mueller, Jeffrey Ladish, Joshua Landau, Kamal Ndousse, Kamile Lukosuite, Liane Lovitt, Michael Sellitto, Nelson Elhage, Nicholas Schiefer, Noemi Mercado, Nova DasSarma, Robert Lasenby, Robin Larson, Sam Ringer, Scott Johnston, Shauna Kravec, Sheer El Showk, Stanislav Fort, Tamera Lanham, Timothy Telleen-Lawton, Tom Conerly, Tom Henighan, Tristan Hume, Samuel R. Bowman, Zac Hatfield-Dodds, Ben Mann, Dario Amodei, Nicholas Joseph, Sam McCandlish, Tom Brown, and Jared Kaplan. 2022 · 2022
Cited alongside, same era.
Auditing search query suggestion bias through recursive algorithm interrogation
Fabian Haak and Philipp Schaer. 2022 · 2022
Cited alongside, same era.
LoRA: Low-rank adaptation of large language models
Edward J Hu, yelong shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2022 · 2022
Cited alongside, same era.
Holistic evaluation of language models
Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, Benjamin Newman, Binhang Yuan, Bobby Yan, Ce Zhang, Christian Cosgrove, Christopher D. Manning, Christopher Ré, Diana Acosta-Navas, Drew A. Hudson, Eric Zelikman, Esin Durmus, Faisal Ladhak, Frieda Rong, Hongyu Ren, Huaxiu Yao, Jue Wang, Keshav Santhanam, Laurel J. Orr, Lucia Zheng, Mert Yüksekgönül, Mirac Suzgun, Nathan Kim, Neel Guha, Niladri S. Chatterji, Omar Khattab, Peter Henderson, Qian Huang, Ryan Chi, Sang Michael Xie, Shibani Santurkar, Surya Ganguli, Tatsunori Hashimoto, Thomas Icard, Tianyi Zhang, Vishrav Chaudhary, William Wang, Xuechen Li, Yifan Mai, Yuhui Zhang, and Yuta Koreeda. 2022 · 2022
Cited alongside, same era.
Diversified subgraph query generation with group fairness
Hanchao Ma, Sheng Guan, Christopher Toomey, and Yinghui Wu. 2022 · 2022
Cited alongside, same era.
OpenAI. 2023 · 2023
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Large language models are effective text rankers with pairwise ranking prompting
Zhen Qin, Rolf Jagerman, Kai Hui, Honglei Zhuang, Junru Wu, Jiaming Shen, Tianqi Liu, Jialu Liu, Donald Metzler, Xuanhui Wang, and Michael Bendersky. 2023 · 2023
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Knowledge of cultural moral norms in large language models
Aida Ramezani and Yang Xu. 2023 · 2023
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Nlpositionality: Characterizing design biases of datasets and models
Sebastin Santy, Jenny T. Liang, Ronan Le Bras, Katharina Reinecke, and Maarten Sap. 2023 · 2023
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Large language models are not yet human-level evaluators for abstractive summarization
Chenhui Shen, Liying Cheng, Xuan-Phi Nguyen, Yang You, and Lidong Bing. 2023 · 2023
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Is ChatGPT good at search? investigating large language models as re-ranking agents
Weiwei Sun, Lingyong Yan, Xinyu Ma, Shuaiqiang Wang, Pengjie Ren, Zhumin Chen, Dawei Yin, and Zhaochun Ren. 2023 · 2023
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Found in the middle: Permutation self-consistency improves listwise ranking in large language models
Raphael Tang, Xinyu Zhang, Xueguang Ma, Jimmy Lin, and Ferhan Ture. 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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Decodingtrust: A comprehensive assessment of trustworthiness in GPT models
Boxin Wang, Weixin Chen, Hengzhi Pei, Chulin Xie, Mintong Kang, Chenhui Zhang, Chejian Xu, Zidi Xiong, Ritik Dutta, Rylan Schaeffer, Sang T. Truong, Simran Arora, Mantas Mazeika, Dan Hendrycks, Zinan Lin, Yu Cheng, Sanmi Koyejo, Dawn Song, and Bo Li. 2023 · 2023
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Is chatgpt fair for recommendation? evaluating fairness in large language model recommendation
Jizhi Zhang, Keqin Bao, Yang Zhang, Wenjie Wang, Fuli Feng, and Xiangnan He. 2023 · 2023
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A unified meta-learning framework for fair ranking with curriculum learning
Yuan Wang, Zhiqiang Tao, and Yi Fang. 2024 · 2024
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Xuyang Wu, Zhiyuan Peng, Sravanthi Rajanala, Hsin-Tai Wu, and Yi Fang. 2024 · 2024
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BBQ: A hand-built bias benchmark for question answering
Alicia Parrish, Angelica Chen, Nikita Nangia, Vishakh Padmakumar, Jason Phang, Jana Thompson, Phu Mon Htut, and Samuel R. Bowman. 2022 · 2086
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