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With the advent of Large Language Models (LLMs) possessing increasingly impressive capabilities, a number of Large Vision-Language Models (LVLMs) have been proposed to augment LLMs with visual inputs.
Crows-pairs: A challenge dataset for measuring social biases in masked language models
Nikita Nangia, Clara Vania, Rasika Bhalerao, and Samuel R Bowman. 2020 · 2010
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The insidious (and ironic) effects of positive stereotypes
Aaron C Kay, Martin V Day, Mark P Zanna, and A David Nussbaum. 2013 · 2013
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Measuring and mitigating unintended bias in text classification
Lucas Dixon, John Li, Jeffrey Sorensen, Nithum Thain, and Lucy Vasserman. 2018 · 2018
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Stereotype content: Warmth and competence endure
Susan T Fiske. 2018 · 2018
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Eddie murphy and the dangers of counterfactual causal thinking about detecting racial discrimination
Issa Kohler-Hausmann. 2018 · 2018
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Gender bias in coreference resolution
Rachel Rudinger, Jason Naradowsky, Brian Leonard, and Benjamin Van Durme. 2018 · 2018
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Gender bias in coreference resolution: Evaluation and debiasing methods
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang. 2018 · 2018
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Counterfactual fairness in text classification through robustness
Sahaj Garg, Vincent Perot, Nicole Limtiaco, Ankur Taly, Ed H Chi, and Alex Beutel. 2019 · 2019
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Fairness-aware ranking in search & recommendation systems with application to linkedin talent search
Sahin Cem Geyik, Stuart Ambler, and Krishnaram Kenthapadi. 2019 · 2019
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The risk of racial bias in hate speech detection
Maarten Sap, Dallas Card, Saadia Gabriel, Yejin Choi, and Noah A. Smith. 2019 · 2019
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Language (technology) is power: A critical survey of “bias” in nlp
Su Lin Blodgett, Solon Barocas, Hal Daumé III, and Hanna Wallach. 2020 · 2020
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Quantifying social biases in NLP: A generalization and empirical comparison of extrinsic fairness metrics
Paula Czarnowska, Yogarshi Vyas, and Kashif Shah. 2021 · 2021
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Computer vision and conflicting values: Describing people with automated alt text
Margot Hanley, Solon Barocas, Karen Levy, Shiri Azenkot, and Helen Nissenbaum. 2021 · 2021
Cited alongside, same era.
The use and misuse of counterfactuals in ethical machine learning
Atoosa Kasirzadeh and Andrew Smart. 2021 · 2021
Cited alongside, same era.
StereoSet: Measuring stereotypical bias in pretrained language models
Moin Nadeem, Anna Bethke, and Siva Reddy. 2021 · 2021
Cited alongside, same era.
Comprehensive stereotype content dictionaries using a semi-automated method
Gandalf Nicolas, Xuechunzi Bai, and Susan T Fiske. 2021 · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. 2021 · 2021
Cited alongside, same era.
G-eval: NLG evaluation using gpt-4 with better human alignment
Yang Liu, Dan Iter, Yichong Xu, Shuohang Wang, Ruochen Xu, and Chenguang Zhu. 2023b · 2023
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On the challenges of using black-box APIs for toxicity evaluation in research
Luiza Pozzobon, Beyza Ermis, Patrick Lewis, and Sara Hooker. 2023 · 2023
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Dear: Debiasing vision-language models with additive residuals
Ashish Seth, Mayur Hemani, and Chirag Agarwal. 2023 · 2023
Later among the works it cites.
Is ChatGPT a good NLG evaluator? a preliminary study
Jiaan Wang, Yunlong Liang, Fandong Meng, Zengkui Sun, Haoxiang Shi, Zhixu Li, Jinan Xu, Jianfeng Qu, and Jie Zhou. 2023 · 2023
Later among the works it cites.
Instructblip: Towards general-purpose vision-language models with instruction tuning
Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale N Fung, and Steven Hoi. 2024 · 2024
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Hritik Bansal, Da Yin, Masoud Monajatipoor, and Kai-Wei Chang. 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.
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023 · 2023
Cited alongside, same era.
Easily accessible text-to-image generation amplifies demographic stereotypes at large scale
Federico Bianchi, Pratyusha Kalluri, Esin Durmus, Faisal Ladhak, Myra Cheng, Debora Nozza, Tatsunori Hashimoto, Dan Jurafsky, James Zou, and Aylin Caliskan. 2023 · 2023
Cited alongside, same era.
Multi-modal bias: Introducing a framework for stereotypical bias assessment beyond gender and race in vision–language models
Sepehr Janghorbani and Gerard De Melo. 2023 · 2023
Cited alongside, same era.
Improved baselines with visual instruction tuning
Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee. 2023a
Cited in the paper.
Examining gender and racial bias in large vision–language models using a novel dataset of parallel images
Kathleen Fraser and Svetlana Kiritchenko. 2024 · 2024
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Visogender: A dataset for benchmarking gender bias in image-text pronoun resolution
Siobhan Mackenzie Hall, Fernanda Gonçalves Abrantes, Hanwen Zhu, Grace Sodunke, Aleksandar Shtedritski, and Hannah Rose Kirk. 2024 · 2024
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Llava-gemma: Accelerating multimodal foundation models with a compact language model
Musashi Hinck, Matthew L Olson, David Cobbley, Shao-Yen Tseng, and Vasudev Lal. 2024 · 2024
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Probing and mitigating intersectional social biases in vision-language models with counterfactual examples
Phillip Howard, Avinash Madasu, Tiep Le, Gustavo Lujan Moreno, Anahita Bhiwandiwalla, and Vasudev Lal. 2024 · 2024
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Visual instruction tuning
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. 2024 · 2024
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A unified framework and dataset for assessing gender bias in vision-language models
Ashutosh Sathe, Prachi Jain, and Sunayana Sitaram. 2024 · 2024
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