Zobrazeno 1 - 10
of 52
pro vyhledávání: '"Santurkar, Shibani"'
Autor:
Santurkar, Shibani, Durmus, Esin, Ladhak, Faisal, Lee, Cinoo, Liang, Percy, Hashimoto, Tatsunori
Language models (LMs) are increasingly being used in open-ended contexts, where the opinions reflected by LMs in response to subjective queries can have a profound impact, both on user satisfaction, as well as shaping the views of society at large. I
Externí odkaz:
http://arxiv.org/abs/2303.17548
Autor:
OpenAI, Achiam, Josh, Adler, Steven, Agarwal, Sandhini, Ahmad, Lama, Akkaya, Ilge, Aleman, Florencia Leoni, Almeida, Diogo, Altenschmidt, Janko, Altman, Sam, Anadkat, Shyamal, Avila, Red, Babuschkin, Igor, Balaji, Suchir, Balcom, Valerie, Baltescu, Paul, Bao, Haiming, Bavarian, Mohammad, Belgum, Jeff, Bello, Irwan, Berdine, Jake, Bernadett-Shapiro, Gabriel, Berner, Christopher, Bogdonoff, Lenny, Boiko, Oleg, Boyd, Madelaine, Brakman, Anna-Luisa, Brockman, Greg, Brooks, Tim, Brundage, Miles, Button, Kevin, Cai, Trevor, Campbell, Rosie, Cann, Andrew, Carey, Brittany, Carlson, Chelsea, Carmichael, Rory, Chan, Brooke, Chang, Che, Chantzis, Fotis, Chen, Derek, Chen, Sully, Chen, Ruby, Chen, Jason, Chen, Mark, Chess, Ben, Cho, Chester, Chu, Casey, Chung, Hyung Won, Cummings, Dave, Currier, Jeremiah, Dai, Yunxing, Decareaux, Cory, Degry, Thomas, Deutsch, Noah, Deville, Damien, Dhar, Arka, Dohan, David, Dowling, Steve, Dunning, Sheila, Ecoffet, Adrien, Eleti, Atty, Eloundou, Tyna, Farhi, David, Fedus, Liam, Felix, Niko, Fishman, Simón Posada, Forte, Juston, Fulford, Isabella, Gao, Leo, Georges, Elie, Gibson, Christian, Goel, Vik, Gogineni, Tarun, Goh, Gabriel, Gontijo-Lopes, Rapha, Gordon, Jonathan, Grafstein, Morgan, Gray, Scott, Greene, Ryan, Gross, Joshua, Gu, Shixiang Shane, Guo, Yufei, Hallacy, Chris, Han, Jesse, Harris, Jeff, He, Yuchen, Heaton, Mike, Heidecke, Johannes, Hesse, Chris, Hickey, Alan, Hickey, Wade, Hoeschele, Peter, Houghton, Brandon, Hsu, Kenny, Hu, Shengli, Hu, Xin, Huizinga, Joost, Jain, Shantanu, Jain, Shawn, Jang, Joanne, Jiang, Angela, Jiang, Roger, Jin, Haozhun, Jin, Denny, Jomoto, Shino, Jonn, Billie, Jun, Heewoo, Kaftan, Tomer, Kaiser, Łukasz, Kamali, Ali, Kanitscheider, Ingmar, Keskar, Nitish Shirish, Khan, Tabarak, Kilpatrick, Logan, Kim, Jong Wook, Kim, Christina, Kim, Yongjik, Kirchner, Jan Hendrik, Kiros, Jamie, Knight, Matt, Kokotajlo, Daniel, Kondraciuk, Łukasz, Kondrich, Andrew, Konstantinidis, Aris, Kosic, Kyle, Krueger, Gretchen, Kuo, Vishal, Lampe, Michael, Lan, Ikai, Lee, Teddy, Leike, Jan, Leung, Jade, Levy, Daniel, Li, Chak Ming, Lim, Rachel, Lin, Molly, Lin, Stephanie, Litwin, Mateusz, Lopez, Theresa, Lowe, Ryan, Lue, Patricia, Makanju, Anna, Malfacini, Kim, Manning, Sam, Markov, Todor, Markovski, Yaniv, Martin, Bianca, Mayer, Katie, Mayne, Andrew, McGrew, Bob, McKinney, Scott Mayer, McLeavey, Christine, McMillan, Paul, McNeil, Jake, Medina, David, Mehta, Aalok, Menick, Jacob, Metz, Luke, Mishchenko, Andrey, Mishkin, Pamela, Monaco, Vinnie, Morikawa, Evan, Mossing, Daniel, Mu, Tong, Murati, Mira, Murk, Oleg, Mély, David, Nair, Ashvin, Nakano, Reiichiro, Nayak, Rajeev, Neelakantan, Arvind, Ngo, Richard, Noh, Hyeonwoo, Ouyang, Long, O'Keefe, Cullen, Pachocki, Jakub, Paino, Alex, Palermo, Joe, Pantuliano, Ashley, Parascandolo, Giambattista, Parish, Joel, Parparita, Emy, Passos, Alex, Pavlov, Mikhail, Peng, Andrew, Perelman, Adam, Peres, Filipe de Avila Belbute, Petrov, Michael, Pinto, Henrique Ponde de Oliveira, Michael, Pokorny, Pokrass, Michelle, Pong, Vitchyr H., Powell, Tolly, Power, Alethea, Power, Boris, Proehl, Elizabeth, Puri, Raul, Radford, Alec, Rae, Jack, Ramesh, Aditya, Raymond, Cameron, Real, Francis, Rimbach, Kendra, Ross, Carl, Rotsted, Bob, Roussez, Henri, Ryder, Nick, Saltarelli, Mario, Sanders, Ted, Santurkar, Shibani, Sastry, Girish, Schmidt, Heather, Schnurr, David, Schulman, John, Selsam, Daniel, Sheppard, Kyla, Sherbakov, Toki, Shieh, Jessica, Shoker, Sarah, Shyam, Pranav, Sidor, Szymon, Sigler, Eric, Simens, Maddie, Sitkin, Jordan, Slama, Katarina, Sohl, Ian, Sokolowsky, Benjamin, Song, Yang, Staudacher, Natalie, Such, Felipe Petroski, Summers, Natalie, Sutskever, Ilya, Tang, Jie, Tezak, Nikolas, Thompson, Madeleine B., Tillet, Phil, Tootoonchian, Amin, Tseng, Elizabeth, Tuggle, Preston, Turley, Nick, Tworek, Jerry, Uribe, Juan Felipe Cerón, Vallone, Andrea, Vijayvergiya, Arun, Voss, Chelsea, Wainwright, Carroll, Wang, Justin Jay, Wang, Alvin, Wang, Ben, Ward, Jonathan, Wei, Jason, Weinmann, CJ, Welihinda, Akila, Welinder, Peter, Weng, Jiayi, Weng, Lilian, Wiethoff, Matt, Willner, Dave, Winter, Clemens, Wolrich, Samuel, Wong, Hannah, Workman, Lauren, Wu, Sherwin, Wu, Jeff, Wu, Michael, Xiao, Kai, Xu, Tao, Yoo, Sarah, Yu, Kevin, Yuan, Qiming, Zaremba, Wojciech, Zellers, Rowan, Zhang, Chong, Zhang, Marvin, Zhao, Shengjia, Zheng, Tianhao, Zhuang, Juntang, Zhuk, William, Zoph, Barret
We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs. While less capable than humans in many real-world scenarios, GPT-4 exhibits human-level performance on various profes
Externí odkaz:
http://arxiv.org/abs/2303.08774
Selecting a suitable pretraining dataset is crucial for both general-domain (e.g., GPT-3) and domain-specific (e.g., Codex) language models (LMs). We formalize this problem as selecting a subset of a large raw unlabeled dataset to match a desired tar
Externí odkaz:
http://arxiv.org/abs/2302.03169
Autor:
Liang, Percy, Bommasani, Rishi, Lee, Tony, Tsipras, Dimitris, Soylu, Dilara, Yasunaga, Michihiro, Zhang, Yian, Narayanan, Deepak, Wu, Yuhuai, Kumar, Ananya, Newman, Benjamin, Yuan, Binhang, Yan, Bobby, Zhang, Ce, Cosgrove, Christian, Manning, Christopher D., Ré, Christopher, Acosta-Navas, Diana, Hudson, Drew A., Zelikman, Eric, Durmus, Esin, Ladhak, Faisal, Rong, Frieda, Ren, Hongyu, Yao, Huaxiu, Wang, Jue, Santhanam, Keshav, Orr, Laurel, Zheng, Lucia, Yuksekgonul, Mert, Suzgun, Mirac, Kim, Nathan, Guha, Neel, Chatterji, Niladri, Khattab, Omar, Henderson, Peter, Huang, Qian, Chi, Ryan, Xie, Sang Michael, Santurkar, Shibani, Ganguli, Surya, Hashimoto, Tatsunori, Icard, Thomas, Zhang, Tianyi, Chaudhary, Vishrav, Wang, William, Li, Xuechen, Mai, Yifan, Zhang, Yuhui, Koreeda, Yuta
Publikováno v:
Published in Transactions on Machine Learning Research (TMLR), 2023
Language models (LMs) are becoming the foundation for almost all major language technologies, but their capabilities, limitations, and risks are not well understood. We present Holistic Evaluation of Language Models (HELM) to improve the transparency
Externí odkaz:
http://arxiv.org/abs/2211.09110
Autor:
Santurkar, Shibani
Prompted by its performance on a variety of benchmark tasks, machine learning (ML) is now being applied to tackle real-world problems. Yet, there is growing evidence that benchmark performance does not convey the full picture. Existing ML models turn
Externí odkaz:
https://hdl.handle.net/1721.1/139920
The development of CLIP [Radford et al., 2021] has sparked a debate on whether language supervision can result in vision models with more transferable representations than traditional image-only methods. Our work studies this question through a caref
Externí odkaz:
http://arxiv.org/abs/2207.07635
Autor:
Santurkar, Shibani, Tsipras, Dimitris, Elango, Mahalaxmi, Bau, David, Torralba, Antonio, Madry, Aleksander
We present a methodology for modifying the behavior of a classifier by directly rewriting its prediction rules. Our approach requires virtually no additional data collection and can be applied to a variety of settings, including adapting a model to n
Externí odkaz:
http://arxiv.org/abs/2112.01008
Autor:
Leclerc, Guillaume, Salman, Hadi, Ilyas, Andrew, Vemprala, Sai, Engstrom, Logan, Vineet, Vibhav, Xiao, Kai, Zhang, Pengchuan, Santurkar, Shibani, Yang, Greg, Kapoor, Ashish, Madry, Aleksander
We introduce 3DB: an extendable, unified framework for testing and debugging vision models using photorealistic simulation. We demonstrate, through a wide range of use cases, that 3DB allows users to discover vulnerabilities in computer vision system
Externí odkaz:
http://arxiv.org/abs/2106.03805
We show how fitting sparse linear models over learned deep feature representations can lead to more debuggable neural networks. These networks remain highly accurate while also being more amenable to human interpretation, as we demonstrate quantiativ
Externí odkaz:
http://arxiv.org/abs/2105.04857
We develop a methodology for assessing the robustness of models to subpopulation shift---specifically, their ability to generalize to novel data subpopulations that were not observed during training. Our approach leverages the class structure underly
Externí odkaz:
http://arxiv.org/abs/2008.04859