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1. SeeGULL Multilingual: a Dataset of Geo-Culturally Situated Stereotypes

Author

Bhutani, Mukul, Robinson, Kevin, Prabhakaran, Vinodkumar, Dave, Shachi, and Dev, Sunipa

Subjects
Computer Science - Computation and Language, Computer Science - Computer Vision and Pattern Recognition
Abstract

While generative multilingual models are rapidly being deployed, their safety and fairness evaluations are largely limited to resources collected in English. This is especially problematic for evaluations targeting inherently socio-cultural phenomena such as stereotyping, where it is important to build multi-lingual resources that reflect the stereotypes prevalent in respective language communities. However, gathering these resources, at scale, in varied languages and regions pose a significant challenge as it requires broad socio-cultural knowledge and can also be prohibitively expensive. To overcome this critical gap, we employ a recently introduced approach that couples LLM generations for scale with culturally situated validations for reliability, and build SeeGULL Multilingual, a global-scale multilingual dataset of social stereotypes, containing over 25K stereotypes, spanning 20 languages, with human annotations across 23 regions, and demonstrate its utility in identifying gaps in model evaluations. Content warning: Stereotypes shared in this paper can be offensive.

Published
2024

2. MiTTenS: A Dataset for Evaluating Gender Mistranslation

Author

Robinson, Kevin, Kudugunta, Sneha, Stella, Romina, Dev, Sunipa, and Bastings, Jasmijn

Subjects
Computer Science - Computation and Language, Computer Science - Computers and Society
Abstract

Translation systems, including foundation models capable of translation, can produce errors that result in gender mistranslation, and such errors can be especially harmful. To measure the extent of such potential harms when translating into and out of English, we introduce a dataset, MiTTenS, covering 26 languages from a variety of language families and scripts, including several traditionally under-represented in digital resources. The dataset is constructed with handcrafted passages that target known failure patterns, longer synthetically generated passages, and natural passages sourced from multiple domains. We demonstrate the usefulness of the dataset by evaluating both neural machine translation systems and foundation models, and show that all systems exhibit gender mistranslation and potential harm, even in high resource languages., Comment: GitHub repository https://github.com/google-research-datasets/mittens

Published
2024

3. Gemini: A Family of Highly Capable Multimodal Models

Author

Gemini Team, Anil, Rohan, Borgeaud, Sebastian, Alayrac, Jean-Baptiste, Yu, Jiahui, Soricut, Radu, Schalkwyk, Johan, Dai, Andrew M., Hauth, Anja, Millican, Katie, Silver, David, Johnson, Melvin, Antonoglou, Ioannis, Schrittwieser, Julian, Glaese, Amelia, Chen, Jilin, Pitler, Emily, Lillicrap, Timothy, Lazaridou, Angeliki, Firat, Orhan, Molloy, James, Isard, Michael, Barham, Paul R., Hennigan, Tom, Lee, Benjamin, Viola, Fabio, Reynolds, Malcolm, Xu, Yuanzhong, Doherty, Ryan, Collins, Eli, Meyer, Clemens, Rutherford, Eliza, Moreira, Erica, Ayoub, Kareem, Goel, Megha, Krawczyk, Jack, Du, Cosmo, Chi, Ed, Cheng, Heng-Tze, Ni, Eric, Shah, Purvi, Kane, Patrick, Chan, Betty, Faruqui, Manaal, Severyn, Aliaksei, Lin, Hanzhao, Li, YaGuang, Cheng, Yong, Ittycheriah, Abe, Mahdieh, Mahdis, Chen, Mia, Sun, Pei, Tran, Dustin, Bagri, Sumit, Lakshminarayanan, Balaji, Liu, Jeremiah, Orban, Andras, Güra, Fabian, Zhou, Hao, Song, Xinying, Boffy, Aurelien, Ganapathy, Harish, Zheng, Steven, Choe, HyunJeong, Weisz, Ágoston, Zhu, Tao, Lu, Yifeng, Gopal, Siddharth, Kahn, Jarrod, Kula, Maciej, Pitman, Jeff, Shah, Rushin, Taropa, Emanuel, Merey, Majd Al, Baeuml, Martin, Chen, Zhifeng, Shafey, Laurent El, Zhang, Yujing, Sercinoglu, Olcan, Tucker, George, Piqueras, Enrique, Krikun, Maxim, Barr, Iain, Savinov, Nikolay, Danihelka, Ivo, Roelofs, Becca, White, Anaïs, Andreassen, Anders, von Glehn, Tamara, Yagati, Lakshman, Kazemi, Mehran, Gonzalez, Lucas, Khalman, Misha, Sygnowski, Jakub, Frechette, Alexandre, Smith, Charlotte, Culp, Laura, Proleev, Lev, Luan, Yi, Chen, Xi, Lottes, James, Schucher, Nathan, Lebron, Federico, Rrustemi, Alban, Clay, Natalie, Crone, Phil, Kocisky, Tomas, Zhao, Jeffrey, Perz, Bartek, Yu, Dian, Howard, Heidi, Bloniarz, Adam, Rae, Jack W., Lu, Han, Sifre, Laurent, Maggioni, Marcello, Alcober, Fred, Garrette, Dan, Barnes, Megan, Thakoor, Shantanu, Austin, Jacob, Barth-Maron, Gabriel, Wong, William, Joshi, Rishabh, Chaabouni, Rahma, Fatiha, Deeni, Ahuja, Arun, Tomar, Gaurav Singh, Senter, Evan, Chadwick, Martin, Kornakov, Ilya, Attaluri, Nithya, Iturrate, Iñaki, Liu, Ruibo, Li, Yunxuan, Cogan, Sarah, Chen, Jeremy, Jia, Chao, Gu, Chenjie, Zhang, Qiao, Grimstad, Jordan, Hartman, Ale Jakse, Garcia, Xavier, Pillai, Thanumalayan Sankaranarayana, Devlin, Jacob, Laskin, Michael, Casas, Diego de Las, Valter, Dasha, Tao, Connie, Blanco, Lorenzo, Badia, Adrià Puigdomènech, Reitter, David, Chen, Mianna, Brennan, Jenny, Rivera, Clara, Brin, Sergey, Iqbal, Shariq, Surita, Gabriela, Labanowski, Jane, Rao, Abhi, Winkler, Stephanie, Parisotto, Emilio, Gu, Yiming, Olszewska, Kate, Addanki, Ravi, Miech, Antoine, Louis, Annie, Teplyashin, Denis, Brown, Geoff, Catt, Elliot, Balaguer, Jan, Xiang, Jackie, Wang, Pidong, Ashwood, Zoe, Briukhov, Anton, Webson, Albert, Ganapathy, Sanjay, Sanghavi, Smit, Kannan, Ajay, Chang, Ming-Wei, Stjerngren, Axel, Djolonga, Josip, Sun, Yuting, Bapna, Ankur, Aitchison, Matthew, Pejman, Pedram, Michalewski, Henryk, Yu, Tianhe, Wang, Cindy, Love, Juliette, Ahn, Junwhan, Bloxwich, Dawn, Han, Kehang, Humphreys, Peter, Sellam, Thibault, Bradbury, James, Godbole, Varun, Samangooei, Sina, Damoc, Bogdan, Kaskasoli, Alex, Arnold, Sébastien M. R., Vasudevan, Vijay, Agrawal, Shubham, Riesa, Jason, Lepikhin, Dmitry, Tanburn, Richard, Srinivasan, Srivatsan, Lim, Hyeontaek, Hodkinson, Sarah, Shyam, Pranav, Ferret, Johan, Hand, Steven, Garg, Ankush, Paine, Tom Le, Li, Jian, Li, Yujia, Giang, Minh, Neitz, Alexander, Abbas, Zaheer, York, Sarah, Reid, Machel, Cole, Elizabeth, Chowdhery, Aakanksha, Das, Dipanjan, Rogozińska, Dominika, Nikolaev, Vitaliy, Sprechmann, Pablo, Nado, Zachary, Zilka, Lukas, Prost, Flavien, He, Luheng, Monteiro, Marianne, Mishra, Gaurav, Welty, Chris, Newlan, Josh, Jia, Dawei, Allamanis, Miltiadis, Hu, Clara Huiyi, de Liedekerke, Raoul, Gilmer, Justin, Saroufim, Carl, Rijhwani, Shruti, Hou, Shaobo, Shrivastava, Disha, Baddepudi, Anirudh, Goldin, Alex, Ozturel, Adnan, Cassirer, Albin, Xu, Yunhan, Sohn, Daniel, Sachan, Devendra, Amplayo, Reinald Kim, Swanson, Craig, Petrova, Dessie, Narayan, Shashi, Guez, Arthur, Brahma, Siddhartha, Landon, Jessica, Patel, Miteyan, Zhao, Ruizhe, Villela, Kevin, Wang, Luyu, Jia, Wenhao, Rahtz, Matthew, Giménez, Mai, Yeung, Legg, Keeling, James, Georgiev, Petko, Mincu, Diana, Wu, Boxi, Haykal, Salem, Saputro, Rachel, Vodrahalli, Kiran, Qin, James, Cankara, Zeynep, Sharma, Abhanshu, Fernando, Nick, Hawkins, Will, Neyshabur, Behnam, Kim, Solomon, Hutter, Adrian, Agrawal, Priyanka, Castro-Ros, Alex, Driessche, George van den, Wang, Tao, Yang, Fan, Chang, Shuo-yiin, Komarek, Paul, McIlroy, Ross, Lučić, Mario, Zhang, Guodong, Farhan, Wael, Sharman, Michael, Natsev, Paul, Michel, Paul, Bansal, Yamini, Qiao, Siyuan, Cao, Kris, Shakeri, Siamak, Butterfield, Christina, Chung, Justin, Rubenstein, Paul Kishan, Agrawal, Shivani, Mensch, Arthur, Soparkar, Kedar, Lenc, Karel, Chung, Timothy, Pope, Aedan, Maggiore, Loren, Kay, Jackie, Jhakra, Priya, Wang, Shibo, Maynez, Joshua, Phuong, Mary, Tobin, Taylor, Tacchetti, Andrea, Trebacz, Maja, Robinson, Kevin, Katariya, Yash, Riedel, Sebastian, Bailey, Paige, Xiao, Kefan, Ghelani, Nimesh, Aroyo, Lora, Slone, Ambrose, Houlsby, Neil, Xiong, Xuehan, Yang, Zhen, Gribovskaya, Elena, Adler, Jonas, Wirth, Mateo, Lee, Lisa, Li, Music, Kagohara, Thais, Pavagadhi, Jay, Bridgers, Sophie, Bortsova, Anna, Ghemawat, Sanjay, Ahmed, Zafarali, Liu, Tianqi, Powell, Richard, Bolina, Vijay, Iinuma, Mariko, Zablotskaia, Polina, Besley, James, Chung, Da-Woon, Dozat, Timothy, Comanescu, Ramona, Si, Xiance, Greer, Jeremy, Su, Guolong, Polacek, Martin, Kaufman, Raphaël Lopez, Tokumine, Simon, Hu, Hexiang, Buchatskaya, Elena, Miao, Yingjie, Elhawaty, Mohamed, Siddhant, Aditya, Tomasev, Nenad, Xing, Jinwei, Greer, Christina, Miller, Helen, Ashraf, Shereen, Roy, Aurko, Zhang, Zizhao, Ma, Ada, Filos, Angelos, Besta, Milos, Blevins, Rory, Klimenko, Ted, Yeh, Chih-Kuan, Changpinyo, Soravit, Mu, Jiaqi, Chang, Oscar, Pajarskas, Mantas, Muir, Carrie, Cohen, Vered, Lan, Charline Le, Haridasan, Krishna, Marathe, Amit, Hansen, Steven, Douglas, Sholto, Samuel, Rajkumar, Wang, Mingqiu, Austin, Sophia, Lan, Chang, Jiang, Jiepu, Chiu, Justin, Lorenzo, Jaime Alonso, Sjösund, Lars Lowe, Cevey, Sébastien, Gleicher, Zach, Avrahami, Thi, Boral, Anudhyan, Srinivasan, Hansa, Selo, Vittorio, May, Rhys, Aisopos, Konstantinos, Hussenot, Léonard, Soares, Livio Baldini, Baumli, Kate, Chang, Michael B., Recasens, Adrià, Caine, Ben, Pritzel, Alexander, Pavetic, Filip, Pardo, Fabio, Gergely, Anita, Frye, Justin, Ramasesh, Vinay, Horgan, Dan, Badola, Kartikeya, Kassner, Nora, Roy, Subhrajit, Dyer, Ethan, Campos, Víctor Campos, Tomala, Alex, Tang, Yunhao, Badawy, Dalia El, White, Elspeth, Mustafa, Basil, Lang, Oran, Jindal, Abhishek, Vikram, Sharad, Gong, Zhitao, Caelles, Sergi, Hemsley, Ross, Thornton, Gregory, Feng, Fangxiaoyu, Stokowiec, Wojciech, Zheng, Ce, Thacker, Phoebe, Ünlü, Çağlar, Zhang, Zhishuai, Saleh, Mohammad, Svensson, James, Bileschi, Max, Patil, Piyush, Anand, Ankesh, Ring, Roman, Tsihlas, Katerina, Vezer, Arpi, Selvi, Marco, Shevlane, Toby, Rodriguez, Mikel, Kwiatkowski, Tom, Daruki, Samira, Rong, Keran, Dafoe, Allan, FitzGerald, Nicholas, Gu-Lemberg, Keren, Khan, Mina, Hendricks, Lisa Anne, Pellat, Marie, Feinberg, Vladimir, Cobon-Kerr, James, Sainath, Tara, Rauh, Maribeth, Hashemi, Sayed Hadi, Ives, Richard, Hasson, Yana, Noland, Eric, Cao, Yuan, Byrd, Nathan, Hou, Le, Wang, Qingze, Sottiaux, Thibault, Paganini, Michela, Lespiau, Jean-Baptiste, Moufarek, Alexandre, Hassan, Samer, Shivakumar, Kaushik, van Amersfoort, Joost, Mandhane, Amol, Joshi, Pratik, Goyal, Anirudh, Tung, Matthew, Brock, Andrew, Sheahan, Hannah, Misra, Vedant, Li, Cheng, Rakićević, Nemanja, Dehghani, Mostafa, Liu, Fangyu, Mittal, Sid, Oh, Junhyuk, Noury, Seb, Sezener, Eren, Huot, Fantine, Lamm, Matthew, De Cao, Nicola, Chen, Charlie, Mudgal, Sidharth, Stella, Romina, Brooks, Kevin, Vasudevan, Gautam, Liu, Chenxi, Chain, Mainak, Melinkeri, Nivedita, Cohen, Aaron, Wang, Venus, Seymore, Kristie, Zubkov, Sergey, Goel, Rahul, Yue, Summer, Krishnakumaran, Sai, Albert, Brian, Hurley, Nate, Sano, Motoki, Mohananey, Anhad, Joughin, Jonah, Filonov, Egor, Kępa, Tomasz, Eldawy, Yomna, Lim, Jiawern, Rishi, Rahul, Badiezadegan, Shirin, Bos, Taylor, Chang, Jerry, Jain, Sanil, Padmanabhan, Sri Gayatri Sundara, Puttagunta, Subha, Krishna, Kalpesh, Baker, Leslie, Kalb, Norbert, Bedapudi, Vamsi, Kurzrok, Adam, Lei, Shuntong, Yu, Anthony, Litvin, Oren, Zhou, Xiang, Wu, Zhichun, Sobell, Sam, Siciliano, Andrea, Papir, Alan, Neale, Robby, Bragagnolo, Jonas, Toor, Tej, Chen, Tina, Anklin, Valentin, Wang, Feiran, Feng, Richie, Gholami, Milad, Ling, Kevin, Liu, Lijuan, Walter, Jules, Moghaddam, Hamid, Kishore, Arun, Adamek, Jakub, Mercado, Tyler, Mallinson, Jonathan, Wandekar, Siddhinita, Cagle, Stephen, Ofek, Eran, Garrido, Guillermo, Lombriser, Clemens, Mukha, Maksim, Sun, Botu, Mohammad, Hafeezul Rahman, Matak, Josip, Qian, Yadi, Peswani, Vikas, Janus, Pawel, Yuan, Quan, Schelin, Leif, David, Oana, Garg, Ankur, He, Yifan, Duzhyi, Oleksii, Älgmyr, Anton, Lottaz, Timothée, Li, Qi, Yadav, Vikas, Xu, Luyao, Chinien, Alex, Shivanna, Rakesh, Chuklin, Aleksandr, Li, Josie, Spadine, Carrie, Wolfe, Travis, Mohamed, Kareem, Das, Subhabrata, Dai, Zihang, He, Kyle, von Dincklage, Daniel, Upadhyay, Shyam, Maurya, Akanksha, Chi, Luyan, Krause, Sebastian, Salama, Khalid, Rabinovitch, Pam G, M, Pavan Kumar Reddy, Selvan, Aarush, Dektiarev, Mikhail, Ghiasi, Golnaz, Guven, Erdem, Gupta, Himanshu, Liu, Boyi, Sharma, Deepak, Shtacher, Idan Heimlich, Paul, Shachi, Akerlund, Oscar, Aubet, François-Xavier, Huang, Terry, Zhu, Chen, Zhu, Eric, Teixeira, Elico, Fritze, Matthew, Bertolini, Francesco, Marinescu, Liana-Eleonora, Bölle, Martin, Paulus, Dominik, Gupta, Khyatti, Latkar, Tejasi, Chang, Max, Sanders, Jason, Wilson, Roopa, Wu, Xuewei, Tan, Yi-Xuan, Thiet, Lam Nguyen, Doshi, Tulsee, Lall, Sid, Mishra, Swaroop, Chen, Wanming, Luong, Thang, Benjamin, Seth, Lee, Jasmine, Andrejczuk, Ewa, Rabiej, Dominik, Ranjan, Vipul, Styrc, Krzysztof, Yin, Pengcheng, Simon, Jon, Harriott, Malcolm Rose, Bansal, Mudit, Robsky, Alexei, Bacon, Geoff, Greene, David, Mirylenka, Daniil, Zhou, Chen, Sarvana, Obaid, Goyal, Abhimanyu, Andermatt, Samuel, Siegler, Patrick, Horn, Ben, Israel, Assaf, Pongetti, Francesco, Chen, Chih-Wei "Louis", Selvatici, Marco, Silva, Pedro, Wang, Kathie, Tolins, Jackson, Guu, Kelvin, Yogev, Roey, Cai, Xiaochen, Agostini, Alessandro, Shah, Maulik, Nguyen, Hung, Donnaile, Noah Ó, Pereira, Sébastien, Friso, Linda, Stambler, Adam, Kuang, Chenkai, Romanikhin, Yan, Geller, Mark, Yan, ZJ, Jang, Kane, Lee, Cheng-Chun, Fica, Wojciech, Malmi, Eric, Tan, Qijun, Banica, Dan, Balle, Daniel, Pham, Ryan, Huang, Yanping, Avram, Diana, Shi, Hongzhi, Singh, Jasjot, Hidey, Chris, Ahuja, Niharika, Saxena, Pranab, Dooley, Dan, Potharaju, Srividya Pranavi, O'Neill, Eileen, Gokulchandran, Anand, Foley, Ryan, Zhao, Kai, Dusenberry, Mike, Liu, Yuan, Mehta, Pulkit, Kotikalapudi, Ragha, Safranek-Shrader, Chalence, Goodman, Andrew, Kessinger, Joshua, Globen, Eran, Kolhar, Prateek, Gorgolewski, Chris, Ibrahim, Ali, Song, Yang, Eichenbaum, Ali, Brovelli, Thomas, Potluri, Sahitya, Lahoti, Preethi, Baetu, Cip, Ghorbani, Ali, Chen, Charles, Crawford, Andy, Pal, Shalini, Sridhar, Mukund, Gurita, Petru, Mujika, Asier, Petrovski, Igor, Cedoz, Pierre-Louis, Li, Chenmei, Chen, Shiyuan, Santo, Niccolò Dal, Goyal, Siddharth, Punjabi, Jitesh, Kappaganthu, Karthik, Kwak, Chester, LV, Pallavi, Velury, Sarmishta, Choudhury, Himadri, Hall, Jamie, Shah, Premal, Figueira, Ricardo, Thomas, Matt, Lu, Minjie, Zhou, Ting, Kumar, Chintu, Jurdi, Thomas, Chikkerur, Sharat, Ma, Yenai, Yu, Adams, Kwak, Soo, Ähdel, Victor, Rajayogam, Sujeevan, Choma, Travis, Liu, Fei, Barua, Aditya, Ji, Colin, Park, Ji Ho, Hellendoorn, Vincent, Bailey, Alex, Bilal, Taylan, Zhou, Huanjie, Khatir, Mehrdad, Sutton, Charles, Rzadkowski, Wojciech, Macintosh, Fiona, Shagin, Konstantin, Medina, Paul, Liang, Chen, Zhou, Jinjing, Shah, Pararth, Bi, Yingying, Dankovics, Attila, Banga, Shipra, Lehmann, Sabine, Bredesen, Marissa, Lin, Zifan, Hoffmann, John Eric, Lai, Jonathan, Chung, Raynald, Yang, Kai, Balani, Nihal, Bražinskas, Arthur, Sozanschi, Andrei, Hayes, Matthew, Alcalde, Héctor Fernández, Makarov, Peter, Chen, Will, Stella, Antonio, Snijders, Liselotte, Mandl, Michael, Kärrman, Ante, Nowak, Paweł, Wu, Xinyi, Dyck, Alex, Vaidyanathan, Krishnan, R, Raghavender, Mallet, Jessica, Rudominer, Mitch, Johnston, Eric, Mittal, Sushil, Udathu, Akhil, Christensen, Janara, Verma, Vishal, Irving, Zach, Santucci, Andreas, Elsayed, Gamaleldin, Davoodi, Elnaz, Georgiev, Marin, Tenney, Ian, Hua, Nan, Cideron, Geoffrey, Leurent, Edouard, Alnahlawi, Mahmoud, Georgescu, Ionut, Wei, Nan, Zheng, Ivy, Scandinaro, Dylan, Jiang, Heinrich, Snoek, Jasper, Sundararajan, Mukund, Wang, Xuezhi, Ontiveros, Zack, Karo, Itay, Cole, Jeremy, Rajashekhar, Vinu, Tumeh, Lara, Ben-David, Eyal, Jain, Rishub, Uesato, Jonathan, Datta, Romina, Bunyan, Oskar, Wu, Shimu, Zhang, John, Stanczyk, Piotr, Zhang, Ye, Steiner, David, Naskar, Subhajit, Azzam, Michael, Johnson, Matthew, Paszke, Adam, Chiu, Chung-Cheng, Elias, Jaume Sanchez, Mohiuddin, Afroz, Muhammad, Faizan, Miao, Jin, Lee, Andrew, Vieillard, Nino, Park, Jane, Zhang, Jiageng, Stanway, Jeff, Garmon, Drew, Karmarkar, Abhijit, Dong, Zhe, Lee, Jong, Kumar, Aviral, Zhou, Luowei, Evens, Jonathan, Isaac, William, Irving, Geoffrey, Loper, Edward, Fink, Michael, Arkatkar, Isha, Chen, Nanxin, Shafran, Izhak, Petrychenko, Ivan, Chen, Zhe, Jia, Johnson, Levskaya, Anselm, Zhu, Zhenkai, Grabowski, Peter, Mao, Yu, Magni, Alberto, Yao, Kaisheng, Snaider, Javier, Casagrande, Norman, Palmer, Evan, Suganthan, Paul, Castaño, Alfonso, Giannoumis, Irene, Kim, Wooyeol, Rybiński, Mikołaj, Sreevatsa, Ashwin, Prendki, Jennifer, Soergel, David, Goedeckemeyer, Adrian, Gierke, Willi, Jafari, Mohsen, Gaba, Meenu, Wiesner, Jeremy, Wright, Diana Gage, Wei, Yawen, Vashisht, Harsha, Kulizhskaya, Yana, Hoover, Jay, Le, Maigo, Li, Lu, Iwuanyanwu, Chimezie, Liu, Lu, Ramirez, Kevin, Khorlin, Andrey, Cui, Albert, LIN, Tian, Wu, Marcus, Aguilar, Ricardo, Pallo, Keith, Chakladar, Abhishek, Perng, Ginger, Abellan, Elena Allica, Zhang, Mingyang, Dasgupta, Ishita, Kushman, Nate, Penchev, Ivo, Repina, Alena, Wu, Xihui, van der Weide, Tom, Ponnapalli, Priya, Kaplan, Caroline, Simsa, Jiri, Li, Shuangfeng, Dousse, Olivier, Piper, Jeff, Ie, Nathan, Pasumarthi, Rama, Lintz, Nathan, Vijayakumar, Anitha, Andor, Daniel, Valenzuela, Pedro, Lui, Minnie, Paduraru, Cosmin, Peng, Daiyi, Lee, Katherine, Zhang, Shuyuan, Greene, Somer, Nguyen, Duc Dung, Kurylowicz, Paula, Hardin, Cassidy, Dixon, Lucas, Janzer, Lili, Choo, Kiam, Feng, Ziqiang, Zhang, Biao, Singhal, Achintya, Du, Dayou, McKinnon, Dan, Antropova, Natasha, Bolukbasi, Tolga, Keller, Orgad, Reid, David, Finchelstein, Daniel, Raad, Maria Abi, Crocker, Remi, Hawkins, Peter, Dadashi, Robert, Gaffney, Colin, Franko, Ken, Bulanova, Anna, Leblond, Rémi, Chung, Shirley, Askham, Harry, Cobo, Luis C., Xu, Kelvin, Fischer, Felix, Xu, Jun, Sorokin, Christina, Alberti, Chris, Lin, Chu-Cheng, Evans, Colin, Dimitriev, Alek, Forbes, Hannah, Banarse, Dylan, Tung, Zora, Omernick, Mark, Bishop, Colton, Sterneck, Rachel, Jain, Rohan, Xia, Jiawei, Amid, Ehsan, Piccinno, Francesco, Wang, Xingyu, Banzal, Praseem, Mankowitz, Daniel J., Polozov, Alex, Krakovna, Victoria, Brown, Sasha, Bateni, MohammadHossein, Duan, Dennis, Firoiu, Vlad, Thotakuri, Meghana, Natan, Tom, Geist, Matthieu, Girgin, Ser tan, Li, Hui, Ye, Jiayu, Roval, Ofir, Tojo, Reiko, Kwong, Michael, Lee-Thorp, James, Yew, Christopher, Sinopalnikov, Danila, Ramos, Sabela, Mellor, John, Sharma, Abhishek, Wu, Kathy, Miller, David, Sonnerat, Nicolas, Vnukov, Denis, Greig, Rory, Beattie, Jennifer, Caveness, Emily, Bai, Libin, Eisenschlos, Julian, Korchemniy, Alex, Tsai, Tomy, Jasarevic, Mimi, Kong, Weize, Dao, Phuong, Zheng, Zeyu, Liu, Frederick, Zhu, Rui, Teh, Tian Huey, Sanmiya, Jason, Gladchenko, Evgeny, Trdin, Nejc, Toyama, Daniel, Rosen, Evan, Tavakkol, Sasan, Xue, Linting, Elkind, Chen, Woodman, Oliver, Carpenter, John, Papamakarios, George, Kemp, Rupert, Kafle, Sushant, Grunina, Tanya, Sinha, Rishika, Talbert, Alice, Wu, Diane, Owusu-Afriyie, Denese, Thornton, Chloe, Pont-Tuset, Jordi, Narayana, Pradyumna, Li, Jing, Fatehi, Saaber, Wieting, John, Ajmeri, Omar, Uria, Benigno, Ko, Yeongil, Knight, Laura, Héliou, Amélie, Niu, Ning, Gu, Shane, Pang, Chenxi, Li, Yeqing, Levine, Nir, Stolovich, Ariel, Santamaria-Fernandez, Rebeca, Goenka, Sonam, Yustalim, Wenny, Strudel, Robin, Elqursh, Ali, Deck, Charlie, Lee, Hyo, Li, Zonglin, Levin, Kyle, Hoffmann, Raphael, Holtmann-Rice, Dan, Bachem, Olivier, Arora, Sho, Koh, Christy, Yeganeh, Soheil Hassas, Põder, Siim, Tariq, Mukarram, Sun, Yanhua, Ionita, Lucian, Seyedhosseini, Mojtaba, Tafti, Pouya, Liu, Zhiyu, Gulati, Anmol, Liu, Jasmine, Ye, Xinyu, Chrzaszcz, Bart, Wang, Lily, Sethi, Nikhil, Li, Tianrun, Brown, Ben, Singh, Shreya, Fan, Wei, Parisi, Aaron, Stanton, Joe, Koverkathu, Vinod, Choquette-Choo, Christopher A., Li, Yunjie, Lu, TJ, Shroff, Prakash, Varadarajan, Mani, Bahargam, Sanaz, Willoughby, Rob, Gaddy, David, Desjardins, Guillaume, Cornero, Marco, Robenek, Brona, Mittal, Bhavishya, Albrecht, Ben, Shenoy, Ashish, Moiseev, Fedor, Jacobsson, Henrik, Ghaffarkhah, Alireza, Rivière, Morgane, Walton, Alanna, Crepy, Clément, Parrish, Alicia, Zhou, Zongwei, Farabet, Clement, Radebaugh, Carey, Srinivasan, Praveen, van der Salm, Claudia, Fidjeland, Andreas, Scellato, Salvatore, Latorre-Chimoto, Eri, Klimczak-Plucińska, Hanna, Bridson, David, de Cesare, Dario, Hudson, Tom, Mendolicchio, Piermaria, Walker, Lexi, Morris, Alex, Mauger, Matthew, Guseynov, Alexey, Reid, Alison, Odoom, Seth, Loher, Lucia, Cotruta, Victor, Yenugula, Madhavi, Grewe, Dominik, Petrushkina, Anastasia, Duerig, Tom, Sanchez, Antonio, Yadlowsky, Steve, Shen, Amy, Globerson, Amir, Webb, Lynette, Dua, Sahil, Li, Dong, Bhupatiraju, Surya, Hurt, Dan, Qureshi, Haroon, Agarwal, Ananth, Shani, Tomer, Eyal, Matan, Khare, Anuj, Belle, Shreyas Rammohan, Wang, Lei, Tekur, Chetan, Kale, Mihir Sanjay, Wei, Jinliang, Sang, Ruoxin, Saeta, Brennan, Liechty, Tyler, Sun, Yi, Zhao, Yao, Lee, Stephan, Nayak, Pandu, Fritz, Doug, Vuyyuru, Manish Reddy, Aslanides, John, Vyas, Nidhi, Wicke, Martin, Ma, Xiao, Eltyshev, Evgenii, Martin, Nina, Cate, Hardie, Manyika, James, Amiri, Keyvan, Kim, Yelin, Xiong, Xi, Kang, Kai, Luisier, Florian, Tripuraneni, Nilesh, Madras, David, Guo, Mandy, Waters, Austin, Wang, Oliver, Ainslie, Joshua, Baldridge, Jason, Zhang, Han, Pruthi, Garima, Bauer, Jakob, Yang, Feng, Mansour, Riham, Gelman, Jason, Xu, Yang, Polovets, George, Liu, Ji, Cai, Honglong, Chen, Warren, Sheng, XiangHai, Xue, Emily, Ozair, Sherjil, Angermueller, Christof, Li, Xiaowei, Sinha, Anoop, Wang, Weiren, Wiesinger, Julia, Koukoumidis, Emmanouil, Tian, Yuan, Iyer, Anand, Gurumurthy, Madhu, Goldenson, Mark, Shah, Parashar, Blake, MK, Yu, Hongkun, Urbanowicz, Anthony, Palomaki, Jennimaria, Fernando, Chrisantha, Durden, Ken, Mehta, Harsh, Momchev, Nikola, Rahimtoroghi, Elahe, Georgaki, Maria, Raul, Amit, Ruder, Sebastian, Redshaw, Morgan, Lee, Jinhyuk, Zhou, Denny, Jalan, Komal, Li, Dinghua, Hechtman, Blake, Schuh, Parker, Nasr, Milad, Milan, Kieran, Mikulik, Vladimir, Franco, Juliana, Green, Tim, Nguyen, Nam, Kelley, Joe, Mahendru, Aroma, Hu, Andrea, Howland, Joshua, Vargas, Ben, Hui, Jeffrey, Bansal, Kshitij, Rao, Vikram, Ghiya, Rakesh, Wang, Emma, Ye, Ke, Sarr, Jean Michel, Preston, Melanie Moranski, Elish, Madeleine, Li, Steve, Kaku, Aakash, Gupta, Jigar, Pasupat, Ice, Juan, Da-Cheng, Someswar, Milan, M., Tejvi, Chen, Xinyun, Amini, Aida, Fabrikant, Alex, Chu, Eric, Dong, Xuanyi, Muthal, Amruta, Buthpitiya, Senaka, Jauhari, Sarthak, Khandelwal, Urvashi, Hitron, Ayal, Ren, Jie, Rinaldi, Larissa, Drath, Shahar, Dabush, Avigail, Jiang, Nan-Jiang, Godhia, Harshal, Sachs, Uli, Chen, Anthony, Fan, Yicheng, Taitelbaum, Hagai, Noga, Hila, Dai, Zhuyun, Wang, James, Hamer, Jenny, Ferng, Chun-Sung, Elkind, Chenel, Atias, Aviel, Lee, Paulina, Listík, Vít, Carlen, Mathias, van de Kerkhof, Jan, Pikus, Marcin, Zaher, Krunoslav, Müller, Paul, Zykova, Sasha, Stefanec, Richard, Gatsko, Vitaly, Hirnschall, Christoph, Sethi, Ashwin, Xu, Xingyu Federico, Ahuja, Chetan, Tsai, Beth, Stefanoiu, Anca, Feng, Bo, Dhandhania, Keshav, Katyal, Manish, Gupta, Akshay, Parulekar, Atharva, Pitta, Divya, Zhao, Jing, Bhatia, Vivaan, Bhavnani, Yashodha, Alhadlaq, Omar, Li, Xiaolin, Danenberg, Peter, Tu, Dennis, Pine, Alex, Filippova, Vera, Ghosh, Abhipso, Limonchik, Ben, Urala, Bhargava, Lanka, Chaitanya Krishna, Clive, Derik, Li, Edward, Wu, Hao, Hongtongsak, Kevin, Li, Ianna, Thakkar, Kalind, Omarov, Kuanysh, Majmundar, Kushal, Alverson, Michael, Kucharski, Michael, Patel, Mohak, Jain, Mudit, Zabelin, Maksim, Pelagatti, Paolo, Kohli, Rohan, Kumar, Saurabh, Kim, Joseph, Sankar, Swetha, Shah, Vineet, Ramachandruni, Lakshmi, Zeng, Xiangkai, Bariach, Ben, Weidinger, Laura, Vu, Tu, Andreev, Alek, He, Antoine, Hui, Kevin, Kashem, Sheleem, Subramanya, Amar, Hsiao, Sissie, Hassabis, Demis, Kavukcuoglu, Koray, Sadovsky, Adam, Le, Quoc, Strohman, Trevor, Wu, Yonghui, Petrov, Slav, Dean, Jeffrey, and Vinyals, Oriol

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract

This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding. The Gemini family consists of Ultra, Pro, and Nano sizes, suitable for applications ranging from complex reasoning tasks to on-device memory-constrained use-cases. Evaluation on a broad range of benchmarks shows that our most-capable Gemini Ultra model advances the state of the art in 30 of 32 of these benchmarks - notably being the first model to achieve human-expert performance on the well-studied exam benchmark MMLU, and improving the state of the art in every one of the 20 multimodal benchmarks we examined. We believe that the new capabilities of the Gemini family in cross-modal reasoning and language understanding will enable a wide variety of use cases. We discuss our approach toward post-training and deploying Gemini models responsibly to users through services including Gemini, Gemini Advanced, Google AI Studio, and Cloud Vertex AI.

Published
2023

4. AART: AI-Assisted Red-Teaming with Diverse Data Generation for New LLM-powered Applications

Author

Radharapu, Bhaktipriya, Robinson, Kevin, Aroyo, Lora, and Lahoti, Preethi

Subjects
Computer Science - Software Engineering, Computer Science - Artificial Intelligence, Computer Science - Computation and Language
Abstract

Adversarial testing of large language models (LLMs) is crucial for their safe and responsible deployment. We introduce a novel approach for automated generation of adversarial evaluation datasets to test the safety of LLM generations on new downstream applications. We call it AI-assisted Red-Teaming (AART) - an automated alternative to current manual red-teaming efforts. AART offers a data generation and augmentation pipeline of reusable and customizable recipes that reduce human effort significantly and enable integration of adversarial testing earlier in new product development. AART generates evaluation datasets with high diversity of content characteristics critical for effective adversarial testing (e.g. sensitive and harmful concepts, specific to a wide range of cultural and geographic regions and application scenarios). The data generation is steered by AI-assisted recipes to define, scope and prioritize diversity within the application context. This feeds into a structured LLM-generation process that scales up evaluation priorities. Compared to some state-of-the-art tools, AART shows promising results in terms of concept coverage and data quality.

Published
2023

5. A Pretrainer's Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & Toxicity

Author

Longpre, Shayne, Yauney, Gregory, Reif, Emily, Lee, Katherine, Roberts, Adam, Zoph, Barret, Zhou, Denny, Wei, Jason, Robinson, Kevin, Mimno, David, and Ippolito, Daphne

Subjects
Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract

Pretraining is the preliminary and fundamental step in developing capable language models (LM). Despite this, pretraining data design is critically under-documented and often guided by empirically unsupported intuitions. To address this, we pretrain 28 1.5B parameter decoder-only models, training on data curated (1) at different times, (2) with varying toxicity and quality filters, and (3) with different domain compositions. First, we quantify the effect of pretraining data age. A temporal shift between evaluation data and pretraining data leads to performance degradation, which is not overcome by finetuning. Second, we explore the effect of quality and toxicity filters, showing a trade-off between performance on standard benchmarks and risk of toxic generations. Our findings indicate there does not exist a one-size-fits-all solution to filtering training data. We also find that the effects of different types of filtering are not predictable from text domain characteristics. Lastly, we empirically validate that the inclusion of heterogeneous data sources, like books and web, is broadly beneficial and warrants greater prioritization. These findings constitute the largest set of experiments to validate, quantify, and expose many undocumented intuitions about text pretraining, which we hope will help support more informed data-centric decisions in LM development.

Published
2023

6. PaLM 2 Technical Report

Author

Anil, Rohan, Dai, Andrew M., Firat, Orhan, Johnson, Melvin, Lepikhin, Dmitry, Passos, Alexandre, Shakeri, Siamak, Taropa, Emanuel, Bailey, Paige, Chen, Zhifeng, Chu, Eric, Clark, Jonathan H., Shafey, Laurent El, Huang, Yanping, Meier-Hellstern, Kathy, Mishra, Gaurav, Moreira, Erica, Omernick, Mark, Robinson, Kevin, Ruder, Sebastian, Tay, Yi, Xiao, Kefan, Xu, Yuanzhong, Zhang, Yujing, Abrego, Gustavo Hernandez, Ahn, Junwhan, Austin, Jacob, Barham, Paul, Botha, Jan, Bradbury, James, Brahma, Siddhartha, Brooks, Kevin, Catasta, Michele, Cheng, Yong, Cherry, Colin, Choquette-Choo, Christopher A., Chowdhery, Aakanksha, Crepy, Clément, Dave, Shachi, Dehghani, Mostafa, Dev, Sunipa, Devlin, Jacob, Díaz, Mark, Du, Nan, Dyer, Ethan, Feinberg, Vlad, Feng, Fangxiaoyu, Fienber, Vlad, Freitag, Markus, Garcia, Xavier, Gehrmann, Sebastian, Gonzalez, Lucas, Gur-Ari, Guy, Hand, Steven, Hashemi, Hadi, Hou, Le, Howland, Joshua, Hu, Andrea, Hui, Jeffrey, Hurwitz, Jeremy, Isard, Michael, Ittycheriah, Abe, Jagielski, Matthew, Jia, Wenhao, Kenealy, Kathleen, Krikun, Maxim, Kudugunta, Sneha, Lan, Chang, Lee, Katherine, Lee, Benjamin, Li, Eric, Li, Music, Li, Wei, Li, YaGuang, Li, Jian, Lim, Hyeontaek, Lin, Hanzhao, Liu, Zhongtao, Liu, Frederick, Maggioni, Marcello, Mahendru, Aroma, Maynez, Joshua, Misra, Vedant, Moussalem, Maysam, Nado, Zachary, Nham, John, Ni, Eric, Nystrom, Andrew, Parrish, Alicia, Pellat, Marie, Polacek, Martin, Polozov, Alex, Pope, Reiner, Qiao, Siyuan, Reif, Emily, Richter, Bryan, Riley, Parker, Ros, Alex Castro, Roy, Aurko, Saeta, Brennan, Samuel, Rajkumar, Shelby, Renee, Slone, Ambrose, Smilkov, Daniel, So, David R., Sohn, Daniel, Tokumine, Simon, Valter, Dasha, Vasudevan, Vijay, Vodrahalli, Kiran, Wang, Xuezhi, Wang, Pidong, Wang, Zirui, Wang, Tao, Wieting, John, Wu, Yuhuai, Xu, Kelvin, Xu, Yunhan, Xue, Linting, Yin, Pengcheng, Yu, Jiahui, Zhang, Qiao, Zheng, Steven, Zheng, Ce, Zhou, Weikang, Zhou, Denny, Petrov, Slav, and Wu, Yonghui

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

We introduce PaLM 2, a new state-of-the-art language model that has better multilingual and reasoning capabilities and is more compute-efficient than its predecessor PaLM. PaLM 2 is a Transformer-based model trained using a mixture of objectives. Through extensive evaluations on English and multilingual language, and reasoning tasks, we demonstrate that PaLM 2 has significantly improved quality on downstream tasks across different model sizes, while simultaneously exhibiting faster and more efficient inference compared to PaLM. This improved efficiency enables broader deployment while also allowing the model to respond faster, for a more natural pace of interaction. PaLM 2 demonstrates robust reasoning capabilities exemplified by large improvements over PaLM on BIG-Bench and other reasoning tasks. PaLM 2 exhibits stable performance on a suite of responsible AI evaluations, and enables inference-time control over toxicity without additional overhead or impact on other capabilities. Overall, PaLM 2 achieves state-of-the-art performance across a diverse set of tasks and capabilities. When discussing the PaLM 2 family, it is important to distinguish between pre-trained models (of various sizes), fine-tuned variants of these models, and the user-facing products that use these models. In particular, user-facing products typically include additional pre- and post-processing steps. Additionally, the underlying models may evolve over time. Therefore, one should not expect the performance of user-facing products to exactly match the results reported in this report.

Published
2023

7. AAV9:PKP2 improves heart function and survival in a Pkp2-deficient mouse model of arrhythmogenic right ventricular cardiomyopathy

Author

Wu, Iris, Zeng, Aliya, Greer-Short, Amara, Aycinena, J. Alex, Tefera, Anley E., Shenwai, Reva, Farshidfar, Farshad, Van Pell, Melissa, Xu, Emma, Reid, Chris, Rodriguez, Neshel, Lim, Beatriz, Chung, Tae Won, Woods, Joseph, Scott, Aquilla, Jones, Samantha, Dee-Hoskins, Cristina, Gutierrez, Carolina G., Madariaga, Jessie, Robinson, Kevin, Hatter, Yolanda, Butler, Renee, Steltzer, Stephanie, Ho, Jaclyn, Priest, James R., Song, Xiaomei, Jing, Frank, Green, Kristina, Ivey, Kathryn N., Hoey, Timothy, Yang, Jin, and Yang, Zhihong Jane

Published
2024
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8. Scaling Instruction-Finetuned Language Models

Author

Chung, Hyung Won, Hou, Le, Longpre, Shayne, Zoph, Barret, Tay, Yi, Fedus, William, Li, Yunxuan, Wang, Xuezhi, Dehghani, Mostafa, Brahma, Siddhartha, Webson, Albert, Gu, Shixiang Shane, Dai, Zhuyun, Suzgun, Mirac, Chen, Xinyun, Chowdhery, Aakanksha, Castro-Ros, Alex, Pellat, Marie, Robinson, Kevin, Valter, Dasha, Narang, Sharan, Mishra, Gaurav, Yu, Adams, Zhao, Vincent, Huang, Yanping, Dai, Andrew, Yu, Hongkun, Petrov, Slav, Chi, Ed H., Dean, Jeff, Devlin, Jacob, Roberts, Adam, Zhou, Denny, Le, Quoc V., and Wei, Jason

Subjects
Computer Science - Machine Learning, Computer Science - Computation and Language
Abstract

Finetuning language models on a collection of datasets phrased as instructions has been shown to improve model performance and generalization to unseen tasks. In this paper we explore instruction finetuning with a particular focus on (1) scaling the number of tasks, (2) scaling the model size, and (3) finetuning on chain-of-thought data. We find that instruction finetuning with the above aspects dramatically improves performance on a variety of model classes (PaLM, T5, U-PaLM), prompting setups (zero-shot, few-shot, CoT), and evaluation benchmarks (MMLU, BBH, TyDiQA, MGSM, open-ended generation). For instance, Flan-PaLM 540B instruction-finetuned on 1.8K tasks outperforms PALM 540B by a large margin (+9.4% on average). Flan-PaLM 540B achieves state-of-the-art performance on several benchmarks, such as 75.2% on five-shot MMLU. We also publicly release Flan-T5 checkpoints, which achieve strong few-shot performance even compared to much larger models, such as PaLM 62B. Overall, instruction finetuning is a general method for improving the performance and usability of pretrained language models., Comment: Public checkpoints: https://huggingface.co/docs/transformers/model_doc/flan-t5

Published
2022

9. PaLM: Scaling Language Modeling with Pathways

Author

Chowdhery, Aakanksha, Narang, Sharan, Devlin, Jacob, Bosma, Maarten, Mishra, Gaurav, Roberts, Adam, Barham, Paul, Chung, Hyung Won, Sutton, Charles, Gehrmann, Sebastian, Schuh, Parker, Shi, Kensen, Tsvyashchenko, Sasha, Maynez, Joshua, Rao, Abhishek, Barnes, Parker, Tay, Yi, Shazeer, Noam, Prabhakaran, Vinodkumar, Reif, Emily, Du, Nan, Hutchinson, Ben, Pope, Reiner, Bradbury, James, Austin, Jacob, Isard, Michael, Gur-Ari, Guy, Yin, Pengcheng, Duke, Toju, Levskaya, Anselm, Ghemawat, Sanjay, Dev, Sunipa, Michalewski, Henryk, Garcia, Xavier, Misra, Vedant, Robinson, Kevin, Fedus, Liam, Zhou, Denny, Ippolito, Daphne, Luan, David, Lim, Hyeontaek, Zoph, Barret, Spiridonov, Alexander, Sepassi, Ryan, Dohan, David, Agrawal, Shivani, Omernick, Mark, Dai, Andrew M., Pillai, Thanumalayan Sankaranarayana, Pellat, Marie, Lewkowycz, Aitor, Moreira, Erica, Child, Rewon, Polozov, Oleksandr, Lee, Katherine, Zhou, Zongwei, Wang, Xuezhi, Saeta, Brennan, Diaz, Mark, Firat, Orhan, Catasta, Michele, Wei, Jason, Meier-Hellstern, Kathy, Eck, Douglas, Dean, Jeff, Petrov, Slav, and Fiedel, Noah

Subjects
Computer Science - Computation and Language
Abstract

Large language models have been shown to achieve remarkable performance across a variety of natural language tasks using few-shot learning, which drastically reduces the number of task-specific training examples needed to adapt the model to a particular application. To further our understanding of the impact of scale on few-shot learning, we trained a 540-billion parameter, densely activated, Transformer language model, which we call Pathways Language Model PaLM. We trained PaLM on 6144 TPU v4 chips using Pathways, a new ML system which enables highly efficient training across multiple TPU Pods. We demonstrate continued benefits of scaling by achieving state-of-the-art few-shot learning results on hundreds of language understanding and generation benchmarks. On a number of these tasks, PaLM 540B achieves breakthrough performance, outperforming the finetuned state-of-the-art on a suite of multi-step reasoning tasks, and outperforming average human performance on the recently released BIG-bench benchmark. A significant number of BIG-bench tasks showed discontinuous improvements from model scale, meaning that performance steeply increased as we scaled to our largest model. PaLM also has strong capabilities in multilingual tasks and source code generation, which we demonstrate on a wide array of benchmarks. We additionally provide a comprehensive analysis on bias and toxicity, and study the extent of training data memorization with respect to model scale. Finally, we discuss the ethical considerations related to large language models and discuss potential mitigation strategies.

Published
2022

10. GLaM: Efficient Scaling of Language Models with Mixture-of-Experts

Author

Du, Nan, Huang, Yanping, Dai, Andrew M., Tong, Simon, Lepikhin, Dmitry, Xu, Yuanzhong, Krikun, Maxim, Zhou, Yanqi, Yu, Adams Wei, Firat, Orhan, Zoph, Barret, Fedus, Liam, Bosma, Maarten, Zhou, Zongwei, Wang, Tao, Wang, Yu Emma, Webster, Kellie, Pellat, Marie, Robinson, Kevin, Meier-Hellstern, Kathleen, Duke, Toju, Dixon, Lucas, Zhang, Kun, Le, Quoc V, Wu, Yonghui, Chen, Zhifeng, and Cui, Claire

Subjects
Computer Science - Computation and Language
Abstract

Scaling language models with more data, compute and parameters has driven significant progress in natural language processing. For example, thanks to scaling, GPT-3 was able to achieve strong results on in-context learning tasks. However, training these large dense models requires significant amounts of computing resources. In this paper, we propose and develop a family of language models named GLaM (Generalist Language Model), which uses a sparsely activated mixture-of-experts architecture to scale the model capacity while also incurring substantially less training cost compared to dense variants. The largest GLaM has 1.2 trillion parameters, which is approximately 7x larger than GPT-3. It consumes only 1/3 of the energy used to train GPT-3 and requires half of the computation flops for inference, while still achieving better overall zero-shot and one-shot performance across 29 NLP tasks., Comment: Accepted to ICML 2022

Published
2021

11. Overcoming bio/pharmaceutical production hurdles: an industry insider perspective

Author

Robinson, Kevin

Subjects
Business, Chemicals, plastics and rubber industries, Engineering and manufacturing industries, Business, international
Abstract

From F-gas regulation to finance and from biologics to personalised medicines, the global drug production environment faces a number of challenges that could potentially stifle innovation and profitability. Dr Kevin [...]

Published
2023

12. Sense and sensing : a critical enquiry into relations between hearing-impaired children, vision and photography

Author

Robinson, Kevin

Subjects
371.9, Photography, Hearing Impairment, Vision, Language, Childhood, Performance
Abstract

This thesis explores and critiques attitudes and assumptions related to hearing-impaired, school-level students and their engagement with the educational discipline of photography. It is not uncommon to find that hearing-impaired children are described as visual learners. This thesis argues that the conceptualisation of children as visual learners merely reflects a wider discourse that aims to legitimise a certain practice of schooling as normative. In appearing as common-sense and value neutral, the discourse has the tendency to perpetuate the marginalisation of those senses deemed less noble and the pedagogical insistence on a premise that posits a link between looking, seeing and knowing, remains. It is argued that distinguishing the sense of sight from that of listening and from the body, and the westernised privileging of seeing has had ramifications across the educational landscape for methodologies employed in facilitating language acquisition for such cohorts of children. This includes the organisation of their photographic learning experiences; the architectural structure of buildings, classrooms, and the systematised pedagogic and educational narratives that are mobilised in the pursuit of pupil progress. Engaging with Mitchell, Serres, Di Bello, Deleuze and Guattari, Merleau-Ponty, Foucault and others, this thesis destabilises the dominant discourses upon which such assumptions are founded and framed unproblematically, as common-sense. What would the educational landscape for hearing-impaired children, as expressed through photography, 'feel' like, 'smell' like, 'taste' like, 'sound' like and of course, 'look' like, if a more nuanced, synesthetic shift were embraced that engaged with sensory modalities beyond that of vision and hearing? This study explores these debates through references to photographic material created by students in secondary level education and through references to photographic activities conducted outside of the formal curriculum. In doing so, it opens up a critical space within which familiarised traditions of knowledge formations for the hearing-impaired child are challenged.

Published
2021

14. Triple Nickel: An airborne legacy

Author

Robinson, Kevin L., Sgt1C

Subjects
BLACKS IN THE ARMED FORCES, AIRBORNE FORCES - United States - History, ARMY - Corps, Divisions, etc - United States - History
Abstract

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Published
1998

15. The future of digital sample management

Author

Robinson, Kevin

Subjects
Company business management, Business, Chemicals, plastics and rubber industries, Engineering and manufacturing industries, Business, international
Abstract

An unsung hero in the drug development process, proper biological sample management is fundamental to ensuring the accuracy and applicability of scientific studies. Dr Kevin Robinson (KSR) recently spoke to [...]

Published
2022

16. Safety of mapping the motor networks in the spinal cord using penetrating microelectrodes in Yucatan minipigs

Author

Mirkiani, Soroush, primary, O’Sullivan, Carly L., additional, Roszko, David A., additional, Faridi, Pouria, additional, Hu, David S., additional, Everaert, Dirk G., additional, Toossi, Amirali, additional, Kang, Ryan, additional, Fang, Tongzhou, additional, Tyreman, Neil, additional, Dalrymple, Ashley N., additional, Robinson, Kevin, additional, Uwiera, Richard R. E., additional, Shah, Hamid, additional, Fox, Richard, additional, Konrad, Peter E., additional, and Mushahwar, Vivian K., additional

Published
2024
Full Text
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18. Using citizen science to better understand Risso’s dolphin (Grampus griseus) presence in northeast Scotland and the Northern Isles

Author

Hodgins, Nicola K., primary, Steel, Emma M., additional, Dyke, Katie, additional, Walters, Alice E. M., additional, Dolman, Sarah J., additional, Hall, Karen, additional, Neave-Webb, Emma, additional, Evans, Peter G. H., additional, Bird, Colin, additional, Robinson, Kevin P., additional, Marwood, Eleanor M., additional, Foubister, Robert, additional, Harrop, Hugh, additional, Knight, Andy, additional, and Munro, Karen, additional

Published
2024
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20. Made in Switzerland: blockchain-enabled healthcare

Author

Robinson, Kevin

Subjects
Novartis AG -- Officials and employees -- Laws, regulations and rules -- Innovations, Privacy, Right of -- Laws, regulations and rules, Pharmaceutical industry -- Officials and employees -- Innovations, Government regulation, Business, Chemicals, plastics and rubber industries, Engineering and manufacturing industries, Business, international
Abstract

Interoperable blockchain-enabled solutions could eliminate waste, enhance security and privacy, build trust and, ultimately, benefit patients and the entire pharmaceutical value chain, say the founders of PharmaLedger. Dr Kevin Robinson [...]

Published
2023

23. Analytical method development in a high potent facility

Author

Robinson, Kevin

Subjects
Business, Chemicals, plastics and rubber industries, Engineering and manufacturing industries, Business, international
Abstract

Analytical method development is a cornerstone of pharmaceutical outsourcing. In this Q&A with Dr Kevin Robinson (KSR), Amanda Blackwell (AB), Head of Analytical Development at PCI Pharma Services' high potent [...]

Published
2023

24. Taking a decentralized approach to commercializing cell and gene therapies

Author

Robinson, Kevin

Subjects
Thermo Fisher Scientific Inc., Scientific equipment and supplies industry -- Supply and demand, Genetic research, Gene therapy, Cancer -- Care and treatment, Business, Chemicals, plastics and rubber industries, Engineering and manufacturing industries, Business, international
Abstract

With the medical world facing some of the worst cancer drug shortages in history, many thousands of patients are not getting the treatments they need. To overcome these challenges, fundamental [...]

Published
2023

25. Lithuania: a land of opportunity for clinical trials: In conversation with Karolina Karl, Head of the Life Sciences Team at Invest Lithuania, Dr Kevin Robinson discovers why the country offers such an attractive environment for sponsor companies and how accelerating clinical trials has emerged as a value proposition

Author

Robinson, Kevin

Subjects
Medical research, Medicine, Experimental, Clinical trials, Business, Chemicals, plastics and rubber industries, Engineering and manufacturing industries, Business, international
Abstract

The first industry sponsored clinical trials in Lithuania began in 1992. (1) Since then, during the subsequent 30 years, the clinical research environment has undergone substantial development, particularly regarding the [...]

Published
2022

26. A digital approach to building better peptides

Author

Robinson, Kevin

Subjects
Homeopathy -- Materia medica and therapeutics, Therapeutics, Peptides, Business, Chemicals, plastics and rubber industries, Engineering and manufacturing industries, Business, international
Abstract

Solid-phase peptide synthesis is a well-established process that's used to manufacture peptides for both clinical development and commercial supply. Currently available technologies, however, are prone to errors and may not [...]

Published
2022

27. Industry insight: packaging, protection and patient safety

Author

Robinson, Kevin

Subjects
Patients -- Care and treatment, Logistics, Product counterfeiting, Business, Chemicals, plastics and rubber industries, Engineering and manufacturing industries, Business, international
Abstract

Much discussed in the pharmaceutical industry is the topic of supply chain security and how to implement it. Dr Kevin Robinson caught up with a couple of experts on the [...]

Published
2024

28. Packaging clause in new EU Clinical Trials Regulation is cause for great concern: Annex VI: the challenge of unintended consequences in the EU Clinical Trials Regulation

Author

Robinson, Kevin

Subjects
Regulatory compliance -- Laws, regulations and rules, Clinical trials -- Laws, regulations and rules, Logistics -- Laws, regulations and rules, Government regulation, Business, Chemicals, plastics and rubber industries, Engineering and manufacturing industries, Business, international, European Union -- Innovations
Abstract

Packaging rule changes in the new EU Clinical Trials Regulation (EU No. 536/2014) have caused concern among new drug sponsors by adding challenging operational hurdles to the new clinical approvals [...]

Published
2022

29. Teacher Moments: A Digital Simulation for Preservice Teachers to Approximate Parent-Teacher Conversations

Author

Thompson, Meredith, Owho-Ovuakporie, Kesiena, Robinson, Kevin, Kim, Yoon Jeon, Slama, Rachel, and Reich, Justin

Abstract

Interactive simulations allow preservice teachers to connect education theory and pedagogy in scaffolded environments. We created digital simulations with scenarios from in-person simulations and used them to prepare novice teachers for conversations with parents. Using a design-based approach we implemented the simulations in an education class, gathered data through surveys and observations, and incorporated feedback into subsequent designs. Novice teachers perceived the simulation as authentic and practiced maintaining composure and articulating pedagogical approaches. Recordings of novice teachers' responses produced by the simulation enabled self-reflection and peer and instructor feedback. Results suggest that these digital simulations hold promise as low-cost, flexible tools for novice teachers to engage in targeted practice in a low-stakes setting.

Published
2019
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32. The MC roundtable: how CDMOs are responding to pharma's top priorities in 2021 (part III)

Author

Robinson, Kevin

Subjects
Pfizer Inc., Contract manufacturing, Learning strategies, Pharmaceutical industry, Business, Chemicals, plastics and rubber industries, Engineering and manufacturing industries, Business, international
Abstract

Dr Kevin Robinson held a virtual roundtable with representatives from a number of top-tier contract (development and) manufacturing organisations (CMOs/CDMOs), including Pfizer CentreOne, Samsung Biologics, MedPharm, Metrics Contract Services, Vibalogics [...]

Published
2021

34. S996 Predictors of Mortality in Patients With Inflammatory Bowel Disease and Clostridioides difficile Colitis: A Retrospective Analysis From Nationwide Inpatient Sample

Author

Balogun, Oluwanifemi, primary, Echikunwoke, Blanche, additional, Babajide, Oyedotun, additional, Soladoye, Elizabeth, additional, Carty, Jordan, additional, Robinson, Kevin, additional, Rau, Rohan, additional, Olafimihan, Ayobami, additional, Jara-Tantoco, Mikaela Nikkola, additional, and Lo, Kevin, additional

Published
2023
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36. ACCELERATING DRUG DISCOVERY WITH MARKET-LEADING CELL LINE GENERATION: In January 2023, integrated drug discovery company Sygnature Discovery announced the acquisition of SB Drug Discovery, a world-leading provider of ion channel and receptor drug discovery research delivering cell line generation and compound screening solutions in neuroscience and other therapeutic indications

Author

Robinson, Kevin

Subjects
Pharmaceutical industry -- Mergers, acquisitions and divestments -- Planning, Company business planning, Company acquisition/merger, Business, Chemicals, plastics and rubber industries, Engineering and manufacturing industries, Business, international
Abstract

The purchase forms part of Sygnature's strategic plan to lead the market in integrated drug discovery solutions and will enable the further expansion of both SB and Sygnature's businesses and [...]

Published
2023

37. Improving the operational efficiency of single-use assemblies in liquid bioprocessing

Author

Robinson, Kevin

Subjects
Merck & Company Inc., Nylon, Business, Chemicals, plastics and rubber industries, Engineering and manufacturing industries, Business, international
Abstract

Merck recently announced the launch of its Ultimus Single-Use Process Container Film, which has been designed to provide extreme durability and leak resistance for the single-use assemblies used during bioprocessing [...]

Published
2023

39. Bringing biotech to the Baltics: the rise of Lithuania

Author

Robinson, Kevin

Subjects
Thermo Fisher Scientific Inc. -- International economic relations, Scientific equipment and supplies industry -- International economic relations, Biotechnology, Biotechnology industry -- International economic relations, Corporations -- Taxation, Cabinet officers, Business, Chemicals, plastics and rubber industries, Engineering and manufacturing industries, Business, international, European Union
Abstract

Lithuania's biotechnology competencies lie in cell and gene therapies, enzymes and the wider pharmaceutical industry, offering companies a rich pool of talent in a still-unsaturated market. Dr Kevin Robinson spoke [...]

Published
2022

40. Ecological habitat partitioning and feeding specialisations of coastal minke whales (Balaenoptera acutorostrata) using a recently designated MPA in northeast Scotland

Author

Robinson, Kevin P., primary, MacDougall, Duncan A. I., additional, Bamford, Connor C. G., additional, Brown, William J., additional, Dolan, Ciaran J., additional, Hall, Rebecca, additional, Haskins, Gary N., additional, Russell, Grace, additional, Sidiropoulos, Theofilos, additional, Sim, Texa M. C., additional, Spinou, Evgenia, additional, Stroud, Elice, additional, Williams, Genevieve, additional, and Culloch, Ross M., additional

Published
2023
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42. BioTIME: A database of biodiversity time series for the Anthropocene

Author

Dornelas, Maria, Antão, Laura H., Moyes, Faye, Bates, Amanda E., Magurran, Anne E., Adam, Dušan, Akhmetzhanova, Asem A., Appeltans, Ward, Arcos, José Manuel, Arnold, Haley, Ayyappan, Narayanan, Badihi, Gal, Baird, Andrew H., Barbosa, Miguel, Barreto, Tiago Egydio, Bässler, Claus, Bellgrove, Alecia, Belmaker, Jonathan, Benedetti-Cecchi, Lisandro, Bett, Brian J., Bjorkman, Anne D., Błazewicz, Magdalena, Blowes, Shane A., Bloch, Christopher P., Bonebrake, Timothy C., Boyd, Susan, Bradford, Matt, Brooks, Andrew J., Brown, James H., Bruelheide, Helge, Budy, Phaedra, Carvalho, Fernando, Castañeda-Moya, Edward, Chen, Chaolun Allen, Chamblee, John F., Chase, Tory J., Collier, Laura Siegwart, Collinge, Sharon K., Condit, Richard, Cooper, Elisabeth J., Cornelissen, J. Hans C., Cotano, Unai, Crow, Shannan Kyle, Damasceno, Gabriella, Davies, Claire H., Davis, Robert A., Day, Frank P., Degraer, Steven, Doherty, Tim S., Dunn, Timothy E., Durigan, Giselda, Duffy, J. Emmett, Edelist, Dor, Edgar, Graham J., Elahi, Robin, Elmendorf, Sarah C., Enemar, Anders, Ernest, S. K. Morgan, Escribano, Rubén, Estiarte, Marc, Evans, Brian S., Fan, Tung-Yung, Farah, Fabiano Turini, Fernandes, Luiz Loureiro, Farneda, Fábio Z., Fidelis, Alessandra, Fitt, Robert, Fosaa, Anna Maria, Franco, Geraldo Antonio Daher Correa, Frank, Grace E., Fraser, William R., García, Hernando, Gatti, Roberto Cazzolla, Givan, Or, Gorgone-Barbosa, Elizabeth, Gould, William A., Gries, Corinna, Grossman, Gary D., Gutierréz, Julio R., Hale, Stephen, Harmon, Mark E., Harte, John, Haskins, Gary, Henshaw, Donald L., Hermanutz, Luise, Hidalgo, Pamela, Higuchi, Pedro, Hoey, Andrew, Van Hoey, Gert, Hofgaard, Annika, Holeck, Kristen, Hollister, Robert D., Holmes, Richard, Hoogenboom, Mia, Hsieh, Chih-hao, Hubbell, Stephen P., Huettmann, Falk, Huffard, Christine L., Hurlbert, Allen H., Ivanauskas, Natália Macedo, Janík, David, Jandt, Ute, Jazdzewska, Anna, Johannessen, Tore, Johnstone, Jill, Jones, Julia, Jones, Faith A. M., Kang, Jungwon, Kartawijaya, Tasrif, Keeley, Erin C., Kelt, Douglas A., Kinnear, Rebecca, Klanderud, Kari, Knutsen, Halvor, Koenig, Christopher C., Kortz, Alessandra R., Král, Kamil, Kuhnz, Linda A., Kuo, Chao-Yang, Kushner, David J., Laguionie-Marchais, Claire, Lancaster, Lesley T., Lee, Cheol Min, Lefcheck, Jonathan S., Lévesque, Esther, Lightfoot, David, Lloret, Francisco, Lloyd, John D., López-Baucells, Adrià, Louzao, Maite, Madin, Joshua S., Magnússon, Borgþór, Malamud, Shahar, Matthews, Iain, McFarland, Kent P., McGill, Brian, McKnight, Diane, McLarney, William O., Meador, Jason, Meserve, Peter L., Metcalfe, Daniel J., Meyer, Christoph F. J., Michelsen, Anders, Milchakova, Nataliya, Moens, Tom, Moland, Even, Moore, Jon, Moreira, Carolina Mathias, Müller, Jörg, Murphy, Grace, Myers-Smith, Isla H., Myster, Randall W., Naumov, Andrew, Neat, Francis, Nelson, James A., Nelson, Michael Paul, Newton, Stephen F., Norden, Natalia, Oliver, Jeffrey C., Olsen, Esben M., Onipchenko, Vladimir G., Pabis, Krzysztof, Pabst, Robert J., Paquette, Alain, Pardede, Sinta, Paterson, David M., Pélissier, Raphaël, Peñuelas, Josep, Pérez-Matus, Alejandro, Pizarro, Oscar, Pomati, Francesco, Post, Eric, Prins, Herbert H. T., Priscu, John C., Provoost, Pieter, Prudic, Kathleen L., Pulliainen, Erkki, Ramesh, B. R., Ramos, Olivia Mendivil, Rassweiler, Andrew, Remillard, Suzanne M., Richardson, Anthony J., Richardson, J. Paul, van Rijn, Itai, Rocha, Ricardo, Rivera-Monroy, Victor H., Rixen, Christian, Robinson, Kevin P., Rodrigues, Ricardo Ribeiro, de Cerqueira Rossa-Feres, Denise, Rudstam, Lars, Ruhl, Henry, Ruz, Catalina S., Sampaio, Erica M., Rybicki, Nancy, Rypel, Andrew, Sal, Sofia, Salgado, Beatriz, Santos, Flavio A. M., Savassi-Coutinho, Ana Paula, Scanga, Sara, Schmidt, Jochen, Schooley, Robert, Setiawan, Fakhrizal, Shao, Kwang-Tsao, Shaver, Gaius R., Sherman, Sally, Sherry, Thomas W., Siciński, Jacek, Sievers, Caya, da Silva, Ana Carolina, da Silva, Fernando Rodrigues, Silveira, Fabio L., Slingsby, Jasper, Smart, Tracey, Snell, Sara J., Soudzilovskaia, Nadejda A., Souza, Gabriel B. G., Souza, Flaviana Maluf, Souza, Vinícius Castro, Stallings, Christopher D., Stanforth, Rowan, Stanley, Emily H., Sterza, José Mauro, Stevens, Maarten, Stuart-Smith, Rick, Suarez, Yzel Rondon, Supp, Sarah, Tamashiro, Jorge Yoshio, Tarigan, Sukmaraharja, Thiede, Gary P., Thorn, Simon, Tolvanen, Anne, Toniato, Maria Teresa Zugliani, Totland, Ørjan, Twilley, Robert R., Vaitkus, Gediminas, Valdivia, Nelson, Vallejo, Martha Isabel, Valone, Thomas J., Van Colen, Carl, Vanaverbeke, Jan, Venturoli, Fabio, Verheye, Hans M., Vianna, Marcelo, Vieira, Rui P., Vrška, Tomáš, Vu, Con Quang, Van Vu, Lien, Waide, Robert B., Waldock, Conor, Watts, Dave, Webb, Sara, Wesołowski, Tomasz, White, Ethan P., Widdicombe, Claire E., Wilgers, Dustin, Williams, Richard, Williams, Stefan B., Williamson, Mark, Willig, Michael R., Willis, Trevor J., Wipf, Sonja, Woods, Kerry D., Woehler, Eric J., Zawada, Kyle, and Zettler, Michael L.

Published
2018

43. Effective Rural Schools and National Board Professional Teaching Standards.

Author

Barker, Bruce O. and Robinson, Kevin L.

Abstract

The teacher standards movement and effective schools research movement both proclaim that all students can learn, irrespective of family background, wealth, gender, or ethnicity. Effective schools research promotes seven correlates as core to schools where students learn and achieve: clear school mission, high expectations for success, instructional leadership, frequent monitoring of student progress, opportunity to learn and student time on task, safe and orderly environment, and home and school relations. The National Board for Professional Teaching Standards (NBPTS) was formed to establish high and rigorous standards for what accomplished teachers should know and be able to do. It is dedicated to promoting respect and recognition for quality teaching. The NBPTS' five propositions of accomplished teaching include the following: teachers are committed to students and learning; teachers know the subjects they teach and how to teach them; teachers are responsible for managing and monitoring student learning; teachers think systematically about their practice and learn from experience; and teachers are members of learning communities. The NBPTS propositions link directly to the effective schools correlates. Attaining National Board certification is a professional honor. As teachers complete the certification process, they must continually connect their classroom practice and student learning to National Board standards. (Contains 13 references.) (SM)

Published
2001

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