Fetching the paper…
Reading the bibliography…
As Large Language Models (LLMs) are increasingly deployed to handle various natural language processing (NLP) tasks, concerns regarding the potential negative societal impacts of LLM-generated content have also arisen.
The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients
I-Cheng Yeh and Che-hui Lien · 2009
Earlier work this paper cites.
Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean · 2013
Earlier work this paper cites.
Man is to computer programmer as woman is to homemaker? debiasing word embeddings
Tolga Bolukbasi, Kai-Wei Chang, James Y Zou, Venkatesh Saligrama, and Adam T Kalai · 2016
Earlier work this paper cites.
Semantics derived automatically from language corpora contain human-like biases
Aylin Caliskan, Joanna J Bryson, and Arvind Narayanan · 2017
Earlier work this paper cites.
Uci machine learning repository
Dheeru Dua, Casey Graff, et al · 2017
Earlier work this paper cites.
Fairness constraints: Mechanisms for fair classification
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rogriguez, and Krishna P Gummadi · 2017
Earlier work this paper cites.
The frontiers of fairness in machine learning
Alexandra Chouldechova and Aaron Roth · 2018
Earlier work this paper cites.
Gender bias in coreference resolution
Rachel Rudinger, Jason Naradowsky, Brian Leonard, and Benjamin Van Durme · 2018
Earlier work this paper cites.
Commonsenseqa: A question answering challenge targeting commonsense knowledge
Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant · 2018
Earlier work this paper cites.
Mind the GAP: A balanced corpus of gendered ambiguous pronouns
Kellie Webster, Marta Recasens, Vera Axelrod, and Jason Baldridge · 2018
Earlier work this paper cites.
Gender bias in coreference resolution: Evaluation and debiasing methods
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang · 2018
Earlier work this paper cites.
Jigsaw unintended bias in toxicity classification
Cjadams, Daniel Borkan, Inversion, Jeffrey Sorensen, Lucas Dixon, Lucy Vasserman, and Nithum · 2019
Earlier work this paper cites.
Natural questions: a benchmark for question answering research
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, et al · 2019
Earlier work this paper cites.
Actionable recourse in linear classification
Berk Ustun, Alexander Spangher, and Yang Liu · 2019
Earlier work this paper cites.
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, et al · 2020
Earlier work this paper cites.
Realtoxicityprompts: Evaluating neural toxic degeneration in language models
Samuel Gehman, Suchin Gururangan, Maarten Sap, Yejin Choi, and Noah A Smith · 2020
Earlier work this paper cites.
Crows-pairs: A challenge dataset for measuring social biases in masked language models
Nikita Nangia, Clara Vania, Rasika Bhalerao, and Samuel Bowman · 2020
Earlier work this paper cites.
Fairness warnings and fair-maml: learning fairly with minimal data
Dylan Slack, Sorelle A Friedler, and Emile Givental · 2020
Earlier work this paper cites.
Learning to summarize with human feedback
Nisan Stiennon, Long Ouyang, Jeffrey Wu, Daniel Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul F Christiano · 2020
Earlier work this paper cites.
Measuring and reducing gendered correlations in pre-trained models
Kellie Webster, Xuezhi Wang, Ian Tenney, Alex Beutel, Emily Pitler, Ellie Pavlick, Jilin Chen, Ed Chi, and Slav Petrov · 2020
Earlier work this paper cites.
Training individually fair ml models with sensitive subspace robustness
Mikhail Yurochkin, Amanda Bower, and Yuekai Sun · 2020
Earlier work this paper cites.
Unfairness discovery and prevention for few-shot regression
Chen Zhao and Feng Chen · 2020
Earlier work this paper cites.
Persistent anti-muslim bias in large language models
Abubakar Abid, Maheen Farooqi, and James Zou · 2021
Cited alongside, same era.
Towards a unified framework for fair and stable graph representation learning
Chirag Agarwal, Himabindu Lakkaraju, and Marinka Zitnik · 2021
Cited alongside, same era.
Redditbias: A real-world resource for bias evaluation and debiasing of conversational language models
Soumya Barikeri, Anne Lauscher, Ivan Vulić, and Goran Glavaš · 2021
Cited alongside, same era.
On the dangers of stochastic parrots: Can language models be too big?
Emily M Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell · 2021
Cited alongside, same era.
Fair mixup: Fairness via interpolation
Ching-Yao Chuang and Youssef Mroueh · 2021
Cited alongside, same era.
Bold: Dataset and metrics for measuring biases in open-ended language generation
A survey on evaluation of large language models
Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Kaijie Zhu, Hao Chen, Linyi Yang, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, et al · 2023
Later among the works it cites.
Should chatgpt be biased? challenges and risks of bias in large language models
Emilio Ferrara · 2023
Later among the works it cites.
Fairprism: evaluating fairness-related harms in text generation
Eve Fleisig, Aubrie Amstutz, Chad Atalla, Su Lin Blodgett, Hal Daumé III, Alexandra Olteanu, Emily Sheng, Dan Vann, and Hanna Wallach · 2023
Later among the works it cites.
Bias and fairness in large language models: A survey
Isabel O Gallegos, Ryan A Rossi, Joe Barrow, Md Mehrab Tanjim, Sungchul Kim, Franck Dernoncourt, Tong Yu, Ruiyi Zhang, and Nesreen K Ahmed · 2023
Later among the works it cites.
Gemini, 2023
Google · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Jwala Dhamala, Tony Sun, Varun Kumar, Satyapriya Krishna, Yada Pruksachatkun, Kai-Wei Chang, and Rahul Gupta · 2021
Cited alongside, same era.
Collecting a large-scale gender bias dataset for coreference resolution and machine translation
Shahar Levy, Koren Lazar, and Gabriel Stanovsky · 2021
Cited alongside, same era.
Collecting a large-scale gender bias dataset for coreference resolution and machine translation
Shahar Levy, Koren Lazar, and Gabriel Stanovsky · 2021
Cited alongside, same era.
Stereoset: Measuring stereotypical bias in pretrained language models
Moin Nadeem, Anna Bethke, and Siva Reddy · 2021
Cited alongside, same era.
Palm: Scaling language modeling with pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, Parker Schuh, Kensen Shi, Sasha Tsvyashchenko, Joshua Maynez, Abhishek Rao, Parker Barnes, Yi Tay, Noam Shazeer, Vinodkumar Prabhakaran, Emily Reif, Nan Du, Ben Hutchinson, Reiner Pope, James Bradbury, Jacob Austin, Michael Isard, Guy Gur-Ari, Pengcheng Yin, Toju Duke, Anselm Levskaya, Sanjay Ghemawat, Sunipa Dev, Henryk Michalewski, Xavier Garcia, Vedant Misra, Kevin Robinson, Liam Fedus, Denny Zhou, Daphne Ippolito, David Luan, Hyeontaek Lim, Barret Zoph, Alexander Spiridonov, Ryan Sepassi, David Dohan, Shivani Agrawal, Mark Omernick, Andrew M. Dai, Thanumalayan Sankaranarayana Pillai, Marie Pellat, Aitor Lewkowycz, Erica Moreira, Rewon Child, Oleksandr Polozov, Katherine Lee, Zongwei Zhou, Xuezhi Wang, Brennan Saeta, Mark Diaz, Orhan Firat, Michele Catasta, Jason Wei, Kathy Meier-Hellstern, Douglas Eck, Jeff Dean, Slav Petrov, and Noah Fiedel · 2022
Cited alongside, same era.
On structural explanation of bias in graph neural networks
Yushun Dong, Song Wang, Yu Wang, Tyler Derr, and Jundong Li · 2022
Cited alongside, same era.
Red teaming language models to reduce harms: Methods, scaling behaviors, and lessons learned
Deep Ganguli, Liane Lovitt, Jackson Kernion, Amanda Askell, Yuntao Bai, Saurav Kadavath, Ben Mann, Ethan Perez, Nicholas Schiefer, Kamal Ndousse, et al · 2022
Cited alongside, same era.
Later among the works it cites.
Trustgpt: A benchmark for trustworthy and responsible large language models
Yue Huang, Qihui Zhang, Lichao Sun, et al · 2023
Later among the works it cites.
Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al · 2023
Later among the works it cites.
In conversation with artificial intelligence: aligning language models with human values
Atoosa Kasirzadeh and Iason Gabriel · 2023
Later among the works it cites.
A survey on fairness in large language models. arxiv. doi: 10.48550
Y Li, M Du, R Song, X Wang, and Y Wang · 2023
Later among the works it cites.
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 · 2023
Later among the works it cites.
Gemini: a family of highly capable multimodal models
Gemini Team, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, et al · 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, et al · 2023
Later among the works it cites.
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, et al · 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, et al · 2023
Later among the works it cites.
Fairness in large language models: A taxonomic survey
Zhibo Chu, Zichong Wang, and Wenbin Zhang · 2024
Closest in time.
Scaling instruction-finetuned language models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al · 2024
Closest in time.
Evaluating gender bias in large language models via chain-of-thought prompting, 2024
Masahiro Kaneko, Danushka Bollegala, Naoaki Okazaki, and Timothy Baldwin · 2024
Closest in time.
Yunqi Li, Lanjing Zhang, and Yongfeng Zhang · 2024
Closest in time.
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
Machel Reid, Nikolay Savinov, Denis Teplyashin, Dmitry Lepikhin, Timothy Lillicrap, Jean-baptiste Alayrac, Radu Soricut, Angeliki Lazaridou, Orhan Firat, Julian Schrittwieser, et al · 2024
Closest in time.
Trustllm: Trustworthiness in large language models
Lichao Sun, Yue Huang, Haoran Wang, Siyuan Wu, Qihui Zhang, Chujie Gao, Yixin Huang, Wenhan Lyu, Yixuan Zhang, Xiner Li, et al · 2024
Closest in time.
In-context learning with retrieved demonstrations for language models: A survey
Xin Xu, Yue Liu, Panupong Pasupat, Mehran Kazemi, et al · 2024
Closest in time.