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Employing Large Language Models (LLM) in various downstream applications such as classification is crucial, especially for smaller companies lacking the expertise and resources required for fine-tuning a model.
Stop and Frisk
US Dept of Justice American Judicature Soc. 1968 · 1968
Earlier work this paper cites.
Guest Editors’ Introduction: On Applied Research in Machine Learning
Foster Provost and Ron Kohavi. 1998 · 1998
Earlier work this paper cites.
Fairness-Aware Classifier with Prejudice Remover Regularizer. In Machine Learning and Knowledge Discovery in Databases , Peter A. Flach, Tijl De Bie, and Nello Cristianini (Eds.)
Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, and Jun Sakuma. 2012 · 2012
Earlier work this paper cites.
Learning Fair Representations. In ICML
Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork. 2013 · 2013
Earlier work this paper cites.
Certifying and Removing Disparate Impact. In ACM SIGKDD
Michael Feldman, Sorelle A. Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian. 2015 · 2015
Earlier work this paper cites.
Equality of Opportunity in Supervised Learning. In NIPS
Moritz Hardt, Eric Price, Eric Price, and Nati Srebro. 2016 · 2016
Earlier work this paper cites.
Deep Reinforcement Learning from Human Preferences. In NIPS
Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei. 2017 · 2017
Earlier work this paper cites.
Algorithmic Decision Making and the Cost of Fairness. In ACM SIGKDD
Sam Corbett-Davies, Emma Pierson, Avi Feller, Sharad Goel, and Aziz Huq. 2017 · 2017
Earlier work this paper cites.
Fairness testing: testing software for discrimination. In FSE 2017
Sainyam Galhotra, Yuriy Brun, and Alexandra Meliou. 2017 · 2017
Earlier work this paper cites.
Counterfactual Fairness. In NIPS
Matt J Kusner, Joshua Loftus, Chris Russell, and Ricardo Silva. 2017 · 2017
Earlier work this paper cites.
On Fairness and Calibration. In NIPS
Geoff Pleiss, Manish Raghavan, Felix Wu, Jon Kleinberg, and Kilian Q Weinberger. 2017 · 2017
Earlier work this paper cites.
Attention is All you Need. In NIPS
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Earlier work this paper cites.
Evaluating the Underlying Gender Bias in Contextualized Word Embeddings. In ACL GeBNLP
Christine Basta, Marta R. Costa-jussà, and Noe Casas. 2019 · 2019
Earlier work this paper cites.
Identifying and Reducing Gender Bias in Word-Level Language Models. In NAACL
Shikha Bordia and Samuel R. Bowman. 2019 · 2019
Earlier work this paper cites.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
Earlier work this paper cites.
Language (Technology) is Power: A Critical Survey of “Bias” in NLP. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Su Lin Blodgett, Solon Barocas, Hal Daumé III, and Hanna Wallach. 2020 · 2020
Earlier work this paper cites.
Language Models are Few-Shot Learners. In NIPS , H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin (Eds.)
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 · 2020
Earlier work this paper cites.
RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models. In EMNLP
Samuel Gehman, Suchin Gururangan, Maarten Sap, Yejin Choi, and Noah A. Smith. 2020 · 2020
Cited alongside, same era.
Social Biases in NLP Models as Barriers for Persons with Disabilities. In ACL
Ben Hutchinson, Vinodkumar Prabhakaran, Emily Denton, Kellie Webster, Yu Zhong, and Stephen Denuyl. 2020 · 2020
Cited alongside, same era.
CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models. In EMNLP
Nikita Nangia, Clara Vania, Rasika Bhalerao, and Samuel R. Bowman. 2020 · 2020
Cited alongside, same era.
Large language models associate Muslims with violence
Abubakar Abid, Maheen Farooqi, and James Zou. 2021 · 2021
Cited alongside, same era.
A General Language Assistant as a Laboratory for Alignment
Amanda Askell, Yuntao Bai, Anna Chen, Dawn Drain, Deep Ganguli, Tom Henighan, Andy Jones, Nicholas Joseph, Ben Mann, Nova DasSarma, Nelson Elhage, Zac Hatfield-Dodds, Danny Hernandez, Jackson Kernion, Kamal Ndousse, Catherine Olsson, Dario Amodei, Tom Brown, Jack Clark, Sam McCandlish, Chris Olah, and Jared Kaplan. 2021 · 2021
Gemini: A Family of Highly Capable Multimodal Models
Google Gemini Team. 2023 · 2023
Later among the works it cites.
TabLLM: Few-shot Classification of Tabular Data with Large Language Models. In PMLR
Stefan Hegselmann, Alejandro Buendia, Hunter Lang, Monica Agrawal, Xiaoyi Jiang, and David Sontag. 2023 · 2023
Later among the works it cites.
Gender bias and stereotypes in Large Language Models. In ACM Collective Intelligence Conference
Hadas Kotek, Rikker Dockum, and David Sun. 2023 · 2023
Later among the works it cites.
Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey. In EACL
Sachin Kumar, Vidhisha Balachandran, Lucille Njoo, Antonios Anastasopoulos, and Yulia Tsvetkov. 2023 · 2023
Later among the works it cites.
Finding Support Examples for In-Context Learning. In EMNLP 2023
Xiaonan Li and Xipeng Qiu. 2023 · 2023
Later among the works it cites.
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Cited alongside, same era.
Fairness in Criminal Justice Risk Assessments: The State of the Art
Richard Berk, Hoda Heidari, Shahin Jabbari, Michael Kearns, and Aaron Roth. 2021 · 2021
Cited alongside, same era.
Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity
Yao Lu, Max Bartolo, Alastair Moore, Sebastian Riedel, and Pontus Stenetorp. 2021 · 2021
Cited alongside, same era.
StereoSet: Measuring stereotypical bias in pretrained language models. In ACL IJCNLP
Moin Nadeem, Anna Bethke, and Siva Reddy. 2021 · 2021
Cited alongside, same era.
Calibrate before use: Improving few-shot performance of language models. In ICML
Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh. 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, et al · 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, et al · 2022
Cited alongside, same era.
Red Teaming Language Models with Language Models. In EMNLP
Ethan Perez, Saffron Huang, Francis Song, Trevor Cai, Roman Ring, John Aslanides, Amelia Glaese, Nat McAleese, and Geoffrey Irving. 2022 · 2022
Cited alongside, same era.
Yanchen Liu, Srishti Gautam, Jiaqi Ma, and Himabindu Lakkaraju. 2023 · 2023
Later among the works it cites.
GPT-4 Technical Report
OpenAI. 2023 · 2023
Later among the works it cites.
Ethical Reasoning over Moral Alignment: A Case and Framework for In-Context Ethical Policies in LLMs. In EMNLP
Abhinav Rao, Aditi Khandelwal, Kumar Tanmay, Utkarsh Agarwal, and Monojit Choudhury. 2023 · 2023
Later among the works it cites.
TABLET: Learning From Instructions For Tabular Data
Dylan Slack and Sameer Singh. 2023 · 2023
Later among the works it cites.
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
Later among the works it cites.
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, and Denny Zhou. 2023 · 2023
Later among the works it cites.
Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation. In ACM RecSys
Jizhi Zhang, Keqin Bao, Yang Zhang, Wenjie Wang, Fuli Feng, and Xiangnan He. 2023 · 2023
Later among the works it cites.
A Survey of Large Language Models
Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, Yifan Du, Chen Yang, Yushuo Chen, Zhipeng Chen, Jinhao Jiang, Ruiyang Ren, Yifan Li, Xinyu Tang, Zikang Liu, Peiyu Liu, Jian-Yun Nie, and Ji-Rong Wen. 2023 · 2023
Later among the works it cites.
Large Language Models Are Not Robust Multiple Choice Selectors
Chujie Zheng, Hao Zhou, Fandong Meng, Jie Zhou, and Minlie Huang. 2023 · 2023
Later among the works it cites.
Fairness Certification for Natural Language Processing and Large Language Models
Vincent Freiberger and Erik Buchmann. 2024 · 2024
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Bias Testing and Mitigation in LLM-based Code Generation
Dong Huang, Qingwen Bu, Jie Zhang, Xiaofei Xie, Junjie Chen, and Heming Cui. 2024 · 2024
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