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Vision-language models (VLMs) have gained widespread adoption in both industry and academia.
Predictive inequity in object detection
Benjamin Wilson, Judy Hoffman, and Jamie Morgenstern. 2019 · 1902
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Exposing and correcting the gender bias in image captioning datasets and models
Shruti Bhargava and David A. Forsyth. 2019 · 1912
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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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No classification without representation: Assessing geodiversity issues in open data sets for the developing world
Shreya Shankar, Yoni Halpern, Eric Breck, James Atwood, Jimbo Wilson, and D. Sculley. 2017 · 2017
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Gender shades: Intersectional accuracy disparities in commercial gender classification
Joy Buolamwini and Timnit Gebru. 2018 · 2018
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Women also snowboard: Overcoming bias in captioning models
Lisa Anne Hendricks, Kaylee Burns, Kate Saenko, Trevor Darrell, and Anna Rohrbach. 2018 · 2018
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Racial influence on automated perceptions of emotions
Lauren A. Rhue. 2018 · 2018
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Bleaching text: Abstract features for cross-lingual gender prediction
Rob van der Goot, Nikola Ljubesic, Ian Matroos, Malvina Nissim, and Barbara Plank. 2018 · 2018
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Identifying and reducing gender bias in word-level language models
Shikha Bordia and Samuel R. Bowman. 2019 · 2019
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Does object recognition work for everyone?
Terrance DeVries, Ishan Misra, Changhan Wang, and Laurens van der Maaten. 2019 · 2019
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Towards debiasing sentence representations
Paul Pu Liang, Irene Mengze Li, Emily Zheng, Yao Chong Lim, Ruslan Salakhutdinov, and Louis-Philippe Morency. 2020 · 2020
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Null it out: Guarding protected attributes by iterative nullspace projection
Shauli Ravfogel, Yanai Elazar, Hila Gonen, Michael Twiton, and Yoav Goldberg. 2020 · 2020
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Measuring and reducing gendered correlations in pre-trained models
Kellie Webster, Xuezhi Wang, Ian Tenney, Alex Beutel, Emily Pitler, Ellie Pavlick, Jilin Chen, Ed H. Chi, and Slav Petrov. 2020 · 2020
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Bertscore: Evaluating text generation with bert
Tianyi Zhang*, Varsha Kishore*, Felix Wu*, Kilian Q. Weinberger, and Yoav Artzi. 2020 · 2020
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mindall-e on conceptual captions
Saehoon Kim, Sanghun Cho, Chiheon Kim, Doyup Lee, and Woonhyuk Baek. 2021 · 2021
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Sustainable modular debiasing of language models
Anne Lauscher, Tobias Lueken, and Goran Glavaš. 2021 · 2021
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StereoSet: Measuring stereotypical bias in pretrained language models
Moin Nadeem, Anna Bethke, and Siva Reddy. 2021 · 2021
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Worst of both worlds: Biases compound in pre-trained vision-and-language models
Tejas Srinivasan and Yonatan Bisk. 2021 · 2021
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Image representations learned with unsupervised pre-training contain human-like biases
Ryan Steed and Aylin Caliskan. 2021 · 2021
Cited alongside, same era.
A framework for understanding sources of harm throughout the machine learning life cycle
Harini Suresh and John V. Guttag. 2021 · 2021
Cited alongside, same era.
Mitigating gender bias in captioning systems
Ruixiang Tang, Mengnan Du, Yuening Li, Zirui Liu, Na Zou, and Xia Hu. 2021 · 2021
Cited alongside, same era.
Visogender: A dataset for benchmarking gender bias in image-text pronoun resolution
Siobhan Mackenzie Hall, F. Goncalves Abrantes, Hanwen Zhu, Grace A. Sodunke, Aleksandar Shtedritski, and Hannah Rose Kirk. 2023 · 2023
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Sepehr Janghorbani and Gerard de Melo. 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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Parameter-efficient modularised bias mitigation via AdapterFusion
Deepak Kumar, Oleg Lesota, George Zerveas, Daniel Cohen, Carsten Eickhoff, Markus Schedl, and Navid Rekabsaz. 2023 · 2023
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Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. 2022 · 2022
Cited alongside, same era.
High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. 2022b · 2022
Cited alongside, same era.
Photorealistic text-to-image diffusion models with deep language understanding
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L. Denton, Seyed Kamyar Seyed Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, Jonathan Ho, David J. Fleet, and Mohammad Norouzi. 2022 · 2022
Cited alongside, same era.
"i’m sorry to hear that": Finding new biases in language models with a holistic descriptor dataset
Eric Michael Smith, Melissa Hall, Melanie Kambadur, Eleonora Presani, and Adina Williams. 2022 · 2022
Cited alongside, same era.
Vlstereoset: A study of stereotypical bias in pre-trained vision-language models
Kankan Zhou, Eason Lai, and Jing Jiang. 2022 · 2022
Cited alongside, same era.
Gpt-4v(ision) system card
2023 · 2023
Cited alongside, same era.
Dall-eval: Probing the reasoning skills and social biases of text-to-image generation models
Jaemin Cho, Abhay Zala, and Mohit Bansal. 2023 · 2023
Cited alongside, same era.
Nayeon Lee, Yejin Bang, Holy Lovenia, Samuel Cahyawijaya, Wenliang Dai, and Pascale Fung. 2023 · 2023
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BLIP-2: Bootstrapping language-image pre-training with frozen image encoders and large language models
Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. 2023 · 2023
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OpenAI. 2023 · 2023
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Sdxl: Improving latent diffusion models for high-resolution image synthesis
Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach. 2023 · 2023
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Any-to-any generation via composable diffusion
Zineng Tang, Ziyi Yang, Chenguang Zhu, Michael Zeng, and Mohit Bansal. 2023 · 2023
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Gemini: A family of highly capable multimodal models
Gemini Team. 2023 · 2023
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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 · 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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Bias in generative ai, (work in progress)
Mi Zhou, Vibhanshu Abhishek, and Kannan Srinivasan. 2023 · 2023
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Vip-llava: Making large multimodal models understand arbitrary visual prompts
Mu Cai, Haotian Liu, Dennis Park, Siva Karthik Mustikovela, Gregory P. Meyer, Yuning Chai, and Yong Jae Lee. 2024 · 2024
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Examining gender and racial bias in large vision-language models using a novel dataset of parallel images
Kathleen C. Fraser and Svetlana Kiritchenko. 2024 · 2024
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