Zobrazeno 1 - 10
of 3 234
pro vyhledávání: '"P, Piot"'
Autor:
Kim, Seongyeol, Piot, Philippe, Chen, Gonxiaohui, Doran, Scott, Liu, Wanming, Whiteford, Charles, Wisniewski, Eric, Power, John
Magnetized beams beam with significant canonical angular momentum are critical to electron cooling of hadron beams such as contemplated in next-generation hadron and electron-ion colliders. The transport of magnetized electron beams over long distanc
Externí odkaz:
http://arxiv.org/abs/2410.16827
Autor:
Abdolmaleki, Abbas, Piot, Bilal, Shahriari, Bobak, Springenberg, Jost Tobias, Hertweck, Tim, Joshi, Rishabh, Oh, Junhyuk, Bloesch, Michael, Lampe, Thomas, Heess, Nicolas, Buchli, Jonas, Riedmiller, Martin
Existing preference optimization methods are mainly designed for directly learning from human feedback with the assumption that paired examples (preferred vs. dis-preferred) are available. In contrast, we propose a method that can leverage unpaired p
Externí odkaz:
http://arxiv.org/abs/2410.04166
Autor:
Piot, Paloma, Parapar, Javier
Hate speech is a harmful form of online expression, often manifesting as derogatory posts. It is a significant risk in digital environments. With the rise of Large Language Models (LLMs), there is concern about their potential to replicate hate speec
Externí odkaz:
http://arxiv.org/abs/2410.00775
Autor:
Liu, Tianqi, Xiong, Wei, Ren, Jie, Chen, Lichang, Wu, Junru, Joshi, Rishabh, Gao, Yang, Shen, Jiaming, Qin, Zhen, Yu, Tianhe, Sohn, Daniel, Makarova, Anastasiia, Liu, Jeremiah, Liu, Yuan, Piot, Bilal, Ittycheriah, Abe, Kumar, Aviral, Saleh, Mohammad
Reward models (RMs) play a pivotal role in aligning large language models (LLMs) with human preferences. However, traditional RM training, which relies on response pairs tied to specific prompts, struggles to disentangle prompt-driven preferences fro
Externí odkaz:
http://arxiv.org/abs/2409.13156
Autor:
Xiong, Wei, Shi, Chengshuai, Shen, Jiaming, Rosenberg, Aviv, Qin, Zhen, Calandriello, Daniele, Khalman, Misha, Joshi, Rishabh, Piot, Bilal, Saleh, Mohammad, Jin, Chi, Zhang, Tong, Liu, Tianqi
Recent studies have shown that large language models' (LLMs) mathematical problem-solving capabilities can be enhanced by integrating external tools, such as code interpreters, and employing multi-turn Chain-of-Thought (CoT) reasoning. While current
Externí odkaz:
http://arxiv.org/abs/2409.02392
Autor:
Vieira, J., Cros, B., Muggli, P., Andriyash, I. A., Apsimon, O., Backhouse, M., Benedetti, C., Bulanov, S. S., Caldwell, A., Chen, Min, Cilento, V., Corde, S., D'Arcy, R., Diederichs, S., Ericson, E., Esarey, E., Farmer, J., Fedeli, L., Formenti, A., Foster, B., Garten, M., Geddes, C. G. R., Grismayer, T., Hogan, M. J., Hooker, S., Huebl, A., Jalas, S., Kirchen, M., Lehe, R., Leemans, W., Li, Boyuan, Lindström, C. A., Losito, R., Mitchell, C. E., Mori, W. B., Piot, P., Terzani, D., Thévenet, M., Turner, M., Vay, J. -L., Völker, D., Zhang, Jie, Zhang, W.
The workshop focused on the application of ANAs to particle physics keeping in mind the ultimate goal of a collider at the energy frontier (10\,TeV, e$^+$/e$^-$, e$^-$/e$^-$, or $\gamma\gamma$). The development of ANAs is conducted at universities an
Externí odkaz:
http://arxiv.org/abs/2408.03968
Autor:
Gemma Team, Riviere, Morgane, Pathak, Shreya, Sessa, Pier Giuseppe, Hardin, Cassidy, Bhupatiraju, Surya, Hussenot, Léonard, Mesnard, Thomas, Shahriari, Bobak, Ramé, Alexandre, Ferret, Johan, Liu, Peter, Tafti, Pouya, Friesen, Abe, Casbon, Michelle, Ramos, Sabela, Kumar, Ravin, Lan, Charline Le, Jerome, Sammy, Tsitsulin, Anton, Vieillard, Nino, Stanczyk, Piotr, Girgin, Sertan, Momchev, Nikola, Hoffman, Matt, Thakoor, Shantanu, Grill, Jean-Bastien, Neyshabur, Behnam, Bachem, Olivier, Walton, Alanna, Severyn, Aliaksei, Parrish, Alicia, Ahmad, Aliya, Hutchison, Allen, Abdagic, Alvin, Carl, Amanda, Shen, Amy, Brock, Andy, Coenen, Andy, Laforge, Anthony, Paterson, Antonia, Bastian, Ben, Piot, Bilal, Wu, Bo, Royal, Brandon, Chen, Charlie, Kumar, Chintu, Perry, Chris, Welty, Chris, Choquette-Choo, Christopher A., Sinopalnikov, Danila, Weinberger, David, Vijaykumar, Dimple, Rogozińska, Dominika, Herbison, Dustin, Bandy, Elisa, Wang, Emma, Noland, Eric, Moreira, Erica, Senter, Evan, Eltyshev, Evgenii, Visin, Francesco, Rasskin, Gabriel, Wei, Gary, Cameron, Glenn, Martins, Gus, Hashemi, Hadi, Klimczak-Plucińska, Hanna, Batra, Harleen, Dhand, Harsh, Nardini, Ivan, Mein, Jacinda, Zhou, Jack, Svensson, James, Stanway, Jeff, Chan, Jetha, Zhou, Jin Peng, Carrasqueira, Joana, Iljazi, Joana, Becker, Jocelyn, Fernandez, Joe, van Amersfoort, Joost, Gordon, Josh, Lipschultz, Josh, Newlan, Josh, Ji, Ju-yeong, Mohamed, Kareem, Badola, Kartikeya, Black, Kat, Millican, Katie, McDonell, Keelin, Nguyen, Kelvin, Sodhia, Kiranbir, Greene, Kish, Sjoesund, Lars Lowe, Usui, Lauren, Sifre, Laurent, Heuermann, Lena, Lago, Leticia, McNealus, Lilly, Soares, Livio Baldini, Kilpatrick, Logan, Dixon, Lucas, Martins, Luciano, Reid, Machel, Singh, Manvinder, Iverson, Mark, Görner, Martin, Velloso, Mat, Wirth, Mateo, Davidow, Matt, Miller, Matt, Rahtz, Matthew, Watson, Matthew, Risdal, Meg, Kazemi, Mehran, Moynihan, Michael, Zhang, Ming, Kahng, Minsuk, Park, Minwoo, Rahman, Mofi, Khatwani, Mohit, Dao, Natalie, Bardoliwalla, Nenshad, Devanathan, Nesh, Dumai, Neta, Chauhan, Nilay, Wahltinez, Oscar, Botarda, Pankil, Barnes, Parker, Barham, Paul, Michel, Paul, Jin, Pengchong, Georgiev, Petko, Culliton, Phil, Kuppala, Pradeep, Comanescu, Ramona, Merhej, Ramona, Jana, Reena, Rokni, Reza Ardeshir, Agarwal, Rishabh, Mullins, Ryan, Saadat, Samaneh, Carthy, Sara Mc, Cogan, Sarah, Perrin, Sarah, Arnold, Sébastien M. R., Krause, Sebastian, Dai, Shengyang, Garg, Shruti, Sheth, Shruti, Ronstrom, Sue, Chan, Susan, Jordan, Timothy, Yu, Ting, Eccles, Tom, Hennigan, Tom, Kocisky, Tomas, Doshi, Tulsee, Jain, Vihan, Yadav, Vikas, Meshram, Vilobh, Dharmadhikari, Vishal, Barkley, Warren, Wei, Wei, Ye, Wenming, Han, Woohyun, Kwon, Woosuk, Xu, Xiang, Shen, Zhe, Gong, Zhitao, Wei, Zichuan, Cotruta, Victor, Kirk, Phoebe, Rao, Anand, Giang, Minh, Peran, Ludovic, Warkentin, Tris, Collins, Eli, Barral, Joelle, Ghahramani, Zoubin, Hadsell, Raia, Sculley, D., Banks, Jeanine, Dragan, Anca, Petrov, Slav, Vinyals, Oriol, Dean, Jeff, Hassabis, Demis, Kavukcuoglu, Koray, Farabet, Clement, Buchatskaya, Elena, Borgeaud, Sebastian, Fiedel, Noah, Joulin, Armand, Kenealy, Kathleen, Dadashi, Robert, Andreev, Alek
In this work, we introduce Gemma 2, a new addition to the Gemma family of lightweight, state-of-the-art open models, ranging in scale from 2 billion to 27 billion parameters. In this new version, we apply several known technical modifications to the
Externí odkaz:
http://arxiv.org/abs/2408.00118
Autor:
Richemond, Pierre Harvey, Tang, Yunhao, Guo, Daniel, Calandriello, Daniele, Azar, Mohammad Gheshlaghi, Rafailov, Rafael, Pires, Bernardo Avila, Tarassov, Eugene, Spangher, Lucas, Ellsworth, Will, Severyn, Aliaksei, Mallinson, Jonathan, Shani, Lior, Shamir, Gil, Joshi, Rishabh, Liu, Tianqi, Munos, Remi, Piot, Bilal
The dominant framework for alignment of large language models (LLM), whether through reinforcement learning from human feedback or direct preference optimisation, is to learn from preference data. This involves building datasets where each element is
Externí odkaz:
http://arxiv.org/abs/2405.19107
Autor:
Bac, Seul-Ki, Mardelé, Florian le, Wang, Jiashu, Ozerov, Mykhaylo, Yoshimura, Kota, Mohelský, Ivan, Sun, Xingdan, Piot, Benjamin, Wimmer, Stefan, Ney, Andreas, Orlova, Tatyana, Zhukovskyi, Maksym, Bauer, Günther, Springholz, Gunther, Liu, Xinyu, Orlita, Milan, Park, Kyungwha, Hsu, Yi-Ting, Assaf, Badih A.
Probing the quantum geometry and topology in condensed matter systems has relied heavily on static electronic transport experiments in magnetic fields. Yet, contact-free optical measurements have rarely been explored. Magnetic dichroism (MCD), the no
Externí odkaz:
http://arxiv.org/abs/2405.15689
Autor:
Shani, Lior, Rosenberg, Aviv, Cassel, Asaf, Lang, Oran, Calandriello, Daniele, Zipori, Avital, Noga, Hila, Keller, Orgad, Piot, Bilal, Szpektor, Idan, Hassidim, Avinatan, Matias, Yossi, Munos, Rémi
Reinforcement Learning from Human Feedback (RLHF) has become the standard approach for aligning Large Language Models (LLMs) with human preferences, allowing LLMs to demonstrate remarkable abilities in various tasks. Existing methods work by emulatin
Externí odkaz:
http://arxiv.org/abs/2405.14655