Zobrazeno 1 - 10
of 28
pro vyhledávání: '"Ross, Candace"'
Autor:
Gupta, Vipul, Ross, Candace, Pantoja, David, Passonneau, Rebecca J., Ung, Megan, Williams, Adina
One of the most challenging problems facing NLP today is evaluation. Some of the most pressing issues pertain to benchmark saturation, data contamination, and diversity in the quality of test examples. To address these concerns, we propose Selection
Externí odkaz:
http://arxiv.org/abs/2410.20245
As large language models (LLMs) have grown in prevalence, particular benchmarks have become essential for the evaluation of these models and for understanding model capabilities. Most commonly, we use test accuracy averaged across multiple subtasks i
Externí odkaz:
http://arxiv.org/abs/2406.19470
Autor:
Hemmat, Reyhane Askari, Hall, Melissa, Sun, Alicia, Ross, Candace, Drozdzal, Michal, Romero-Soriano, Adriana
With the growing popularity of text-to-image generative models, there has been increasing focus on understanding their risks and biases. Recent work has found that state-of-the-art models struggle to depict everyday objects with the true diversity of
Externí odkaz:
http://arxiv.org/abs/2406.04551
Autor:
Bordes, Florian, Pang, Richard Yuanzhe, Ajay, Anurag, Li, Alexander C., Bardes, Adrien, Petryk, Suzanne, Mañas, Oscar, Lin, Zhiqiu, Mahmoud, Anas, Jayaraman, Bargav, Ibrahim, Mark, Hall, Melissa, Xiong, Yunyang, Lebensold, Jonathan, Ross, Candace, Jayakumar, Srihari, Guo, Chuan, Bouchacourt, Diane, Al-Tahan, Haider, Padthe, Karthik, Sharma, Vasu, Xu, Hu, Tan, Xiaoqing Ellen, Richards, Megan, Lavoie, Samuel, Astolfi, Pietro, Hemmat, Reyhane Askari, Chen, Jun, Tirumala, Kushal, Assouel, Rim, Moayeri, Mazda, Talattof, Arjang, Chaudhuri, Kamalika, Liu, Zechun, Chen, Xilun, Garrido, Quentin, Ullrich, Karen, Agrawal, Aishwarya, Saenko, Kate, Celikyilmaz, Asli, Chandra, Vikas
Following the recent popularity of Large Language Models (LLMs), several attempts have been made to extend them to the visual domain. From having a visual assistant that could guide us through unfamiliar environments to generative models that produce
Externí odkaz:
http://arxiv.org/abs/2405.17247
Autor:
Hall, Melissa, Bell, Samuel J., Ross, Candace, Williams, Adina, Drozdzal, Michal, Soriano, Adriana Romero
Rapid progress in text-to-image generative models coupled with their deployment for visual content creation has magnified the importance of thoroughly evaluating their performance and identifying potential biases. In pursuit of models that generate i
Externí odkaz:
http://arxiv.org/abs/2405.04457
Autor:
Choshen, Leshem, Cotterell, Ryan, Hu, Michael Y., Linzen, Tal, Mueller, Aaron, Ross, Candace, Warstadt, Alex, Wilcox, Ethan, Williams, Adina, Zhuang, Chengxu
After last year's successful BabyLM Challenge, the competition will be hosted again in 2024/2025. The overarching goals of the challenge remain the same; however, some of the competition rules will be different. The big changes for this year's compet
Externí odkaz:
http://arxiv.org/abs/2404.06214
Autor:
Mañas, Oscar, Astolfi, Pietro, Hall, Melissa, Ross, Candace, Urbanek, Jack, Williams, Adina, Agrawal, Aishwarya, Romero-Soriano, Adriana, Drozdzal, Michal
Impressive advances in text-to-image (T2I) generative models have yielded a plethora of high performing models which are able to generate aesthetically appealing, photorealistic images. Despite the progress, these models still struggle to produce ima
Externí odkaz:
http://arxiv.org/abs/2403.17804
Computer vision models have been known to encode harmful biases, leading to the potentially unfair treatment of historically marginalized groups, such as people of color. However, there remains a lack of datasets balanced along demographic traits tha
Externí odkaz:
http://arxiv.org/abs/2311.15108
Autor:
Yu, Lili, Shi, Bowen, Pasunuru, Ramakanth, Muller, Benjamin, Golovneva, Olga, Wang, Tianlu, Babu, Arun, Tang, Binh, Karrer, Brian, Sheynin, Shelly, Ross, Candace, Polyak, Adam, Howes, Russell, Sharma, Vasu, Xu, Puxin, Tamoyan, Hovhannes, Ashual, Oron, Singer, Uriel, Li, Shang-Wen, Zhang, Susan, James, Richard, Ghosh, Gargi, Taigman, Yaniv, Fazel-Zarandi, Maryam, Celikyilmaz, Asli, Zettlemoyer, Luke, Aghajanyan, Armen
We present CM3Leon (pronounced "Chameleon"), a retrieval-augmented, token-based, decoder-only multi-modal language model capable of generating and infilling both text and images. CM3Leon uses the CM3 multi-modal architecture but additionally shows th
Externí odkaz:
http://arxiv.org/abs/2309.02591
Autor:
Gustafson, Laura, Rolland, Chloe, Ravi, Nikhila, Duval, Quentin, Adcock, Aaron, Fu, Cheng-Yang, Hall, Melissa, Ross, Candace
Computer vision models have known performance disparities across attributes such as gender and skin tone. This means during tasks such as classification and detection, model performance differs for certain classes based on the demographics of the peo
Externí odkaz:
http://arxiv.org/abs/2309.00035