The Moral Turing Test: Evaluating Human-LLM Alignment in Moral Decision-Making

Autor: Garcia, Basile, Qian, Crystal, Palminteri, Stefano

As large language models (LLMs) become increasingly integrated into society, their alignment with human morals is crucial. To better understand this alignment, we created a large corpus of human- and LLM-generated responses to various moral scenarios

Externí odkaz: http://arxiv.org/abs/2410.07304

Understanding the Dataset Practitioners Behind Large Language Model Development

Autor: Qian, Crystal, Reif, Emily, Kahng, Minsuk

As large language models (LLMs) become more advanced and impactful, it is increasingly important to scrutinize the data that they rely upon and produce. What is it to be a dataset practitioner doing this work? We approach this in two parts: first, we

Externí odkaz: http://arxiv.org/abs/2402.16611

Controlling Commercial Cooling Systems Using Reinforcement Learning

Autor: Luo, Jerry, Paduraru, Cosmin, Voicu, Octavian, Chervonyi, Yuri, Munns, Scott, Li, Jerry, Qian, Crystal, Dutta, Praneet, Davis, Jared Quincy, Wu, Ningjia, Yang, Xingwei, Chang, Chu-Ming, Li, Ted, Rose, Rob, Fan, Mingyan, Nakhost, Hootan, Liu, Tinglin, Kirkman, Brian, Altamura, Frank, Cline, Lee, Tonker, Patrick, Gouker, Joel, Uden, Dave, Bryan, Warren Buddy, Law, Jason, Fatiha, Deeni, Satra, Neil, Rothenberg, Juliet, Waraich, Mandeep, Carlin, Molly, Tallapaka, Satish, Witherspoon, Sims, Parish, David, Dolan, Peter, Zhao, Chenyu, Mankowitz, Daniel J.

This paper is a technical overview of DeepMind and Google's recent work on reinforcement learning for controlling commercial cooling systems. Building on expertise that began with cooling Google's data centers more efficiently, we recently conducted

Externí odkaz: http://arxiv.org/abs/2211.07357

Measuring the predictability of life outcomes with a scientific mass collaboration

Autor: Salganik, Matthew J., Lundberg, Ian, Kindel, Alexander T., Ahearn, Caitlin E., Al-Ghoneim, Khaled, Almaatouq, Abdullah, Altschul, Drew M., Brand, Jennie E., Carnegie, Nicole Bohme, Compton, Ryan James, Datta, Debanjan, Davidson, Thomas, Filippova, Anna, Gilroy, Connor, Goode, Brian J., Jahani, Eaman, Kashyap, Ridhi, Kirchner, Antje, McKay, Stephen, Morgan, Allison C., Pentland, Alex, Polimis, Kivan, Raes, Louis, Rigobon, Daniel E., Roberts, Claudia V., Stanescu, Diana M., Suhara, Yoshihiko, Usmania, Adaner, Wang, Erik H., Adem, Muna, Alhajri, Abdulla, AlShebli, Bedoor, Amin, Redwane, Amos, Ryan B., Argyle, Lisa P., Baer-Bositis, Livia, Büchi, Moritz, Chung, Bo-Ryehn, Eggert, William, Faletto, Gregory, Fan, Zhilin, Freese, Jeremy, Gadgil, Tejomay, Gagné, Josh, Gao, Yue, Halpern-Manners, Andrew, Hashim, Sonia P., Hausen, Sonia, He, Guanhua, Higuera, Kimberly, Hogan, Bernie, Horwitz, Ilana M., Hummel, Lisa M., Jain, Naman, Jin, Kun, Jurgens, David, Kaminski, Patrick, Karapetyan, Areg, Kim, E. H., Leizman, Ben, Liu, Naijia, Möser, Malte, Mack, Andrew E., Mahajan, Mayank, Mandell, Noah, Marahrens, Helge, Mercado-Garcia, Diana, Mocz, Viola, Mueller-Gastell, Katariina, Musse, Ahmed, Niu, Qiankun, Nowak, William, Omidvar, Hamidreza, Or, Andrew, Ouyang, Karen, Pinto, Katy M., Porter, Ethan, Porter, Kristin E., Qian, Crystal, Rauf, Tamkinat, Sargsyan, Anahit, Schaffner, Thomas, Schnabel, Landon, Schonfeld, Bryan, Sender, Ben, Tang, Jonathan D., Tsurkov, Emma, van Loon, Austin, Varol, Onur, Wang, Xiafei, Wang, Zhi, Wang, Julia, Wang, Flora, Weissman, Samantha, Whitaker, Kirstie, Wolters, Maria K., Woon, Wei Lee, Wu, James, Wu, Catherine, Yang, Kengran, Yin, Jingwen, Zhao, Bingyu, Zhu, Chenyun, Brooks-Gunn, Jeanne, Engelhardt, Barbara E., Hardt, Moritz, Knox, Dean, Levy, Karen, Narayanan, Arvind, Stewart, Brandon M., Watts, Duncan J., McLanahan, Sara

Publikováno v: Proceedings of the National Academy of Sciences of the United States of America, 2020 Apr . 117(15), 8398-8403.

Externí odkaz: https://www.jstor.org/stable/26930891

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