Zobrazeno 1 - 10
of 4 694
pro vyhledávání: '"de Witt, A."'
Autor:
Zesch, Torsten, Hanses, Michael, Seidel, Niels, Aggarwal, Piush, Veiel, Dirk, de Witt, Claudia
Using the full potential of LLMs in higher education is hindered by challenges with access to LLMs. The two main access modes currently discussed are paying for a cloud-based LLM or providing a locally maintained open LLM. In this paper, we describe
Externí odkaz:
http://arxiv.org/abs/2407.13013
Autor:
Chan, Alan, Kolt, Noam, Wills, Peter, Anwar, Usman, de Witt, Christian Schroeder, Rajkumar, Nitarshan, Hammond, Lewis, Krueger, David, Heim, Lennart, Anderljung, Markus
AI systems are increasingly pervasive, yet information needed to decide whether and how to engage with them may not exist or be accessible. A user may not be able to verify whether a system has certain safety certifications. An investigator may not k
Externí odkaz:
http://arxiv.org/abs/2406.12137
Autor:
Draguns, Andis, Gritsevskiy, Andrew, Motwani, Sumeet Ramesh, Rogers-Smith, Charlie, Ladish, Jeffrey, de Witt, Christian Schroeder
The rapid proliferation of open-source language models significantly increases the risks of downstream backdoor attacks. These backdoors can introduce dangerous behaviours during model deployment and can evade detection by conventional cybersecurity
Externí odkaz:
http://arxiv.org/abs/2406.02619
Autor:
Sokota, Samuel, Sam, Dylan, de Witt, Christian Schroeder, Compton, Spencer, Foerster, Jakob, Kolter, J. Zico
Minimum-entropy coupling (MEC) -- the process of finding a joint distribution with minimum entropy for given marginals -- has applications in areas such as causality and steganography. However, existing algorithms are either computationally intractab
Externí odkaz:
http://arxiv.org/abs/2405.19540
Autor:
Eiras, Francisco, Petrov, Aleksandar, Vidgen, Bertie, de Witt, Christian Schroeder, Pizzati, Fabio, Elkins, Katherine, Mukhopadhyay, Supratik, Bibi, Adel, Csaba, Botos, Steibel, Fabro, Barez, Fazl, Smith, Genevieve, Guadagni, Gianluca, Chun, Jon, Cabot, Jordi, Imperial, Joseph Marvin, Nolazco-Flores, Juan A., Landay, Lori, Jackson, Matthew, Röttger, Paul, Torr, Philip H. S., Darrell, Trevor, Lee, Yong Suk, Foerster, Jakob
In the next few years, applications of Generative AI are expected to revolutionize a number of different areas, ranging from science & medicine to education. The potential for these seismic changes has triggered a lively debate about potential risks
Externí odkaz:
http://arxiv.org/abs/2404.17047
Autor:
Anwar, Usman, Saparov, Abulhair, Rando, Javier, Paleka, Daniel, Turpin, Miles, Hase, Peter, Lubana, Ekdeep Singh, Jenner, Erik, Casper, Stephen, Sourbut, Oliver, Edelman, Benjamin L., Zhang, Zhaowei, Günther, Mario, Korinek, Anton, Hernandez-Orallo, Jose, Hammond, Lewis, Bigelow, Eric, Pan, Alexander, Langosco, Lauro, Korbak, Tomasz, Zhang, Heidi, Zhong, Ruiqi, hÉigeartaigh, Seán Ó, Recchia, Gabriel, Corsi, Giulio, Chan, Alan, Anderljung, Markus, Edwards, Lilian, Petrov, Aleksandar, de Witt, Christian Schroeder, Motwan, Sumeet Ramesh, Bengio, Yoshua, Chen, Danqi, Torr, Philip H. S., Albanie, Samuel, Maharaj, Tegan, Foerster, Jakob, Tramer, Florian, He, He, Kasirzadeh, Atoosa, Choi, Yejin, Krueger, David
This work identifies 18 foundational challenges in assuring the alignment and safety of large language models (LLMs). These challenges are organized into three different categories: scientific understanding of LLMs, development and deployment methods
Externí odkaz:
http://arxiv.org/abs/2404.09932
Autor:
Nasvytis, Linas, Sandbrink, Kai, Foerster, Jakob, Franzmeyer, Tim, de Witt, Christian Schroeder
While reinforcement learning (RL) algorithms have been successfully applied across numerous sequential decision-making problems, their generalization to unforeseen testing environments remains a significant concern. In this paper, we study the proble
Externí odkaz:
http://arxiv.org/abs/2404.07099
Autor:
Motwani, Sumeet Ramesh, Baranchuk, Mikhail, Strohmeier, Martin, Bolina, Vijay, Torr, Philip H. S., Hammond, Lewis, de Witt, Christian Schroeder
Recent capability increases in large language models (LLMs) open up applications in which groups of communicating generative AI agents solve joint tasks. This poses privacy and security challenges concerning the unauthorised sharing of information, o
Externí odkaz:
http://arxiv.org/abs/2402.07510
The increasing prevalence of multi-agent learning systems in society necessitates understanding how to learn effective and safe policies in general-sum multi-agent environments against a variety of opponents, including self-play. General-sum learning
Externí odkaz:
http://arxiv.org/abs/2402.01088
Autor:
Rutherford, Alexander, Ellis, Benjamin, Gallici, Matteo, Cook, Jonathan, Lupu, Andrei, Ingvarsson, Gardar, Willi, Timon, Khan, Akbir, de Witt, Christian Schroeder, Souly, Alexandra, Bandyopadhyay, Saptarashmi, Samvelyan, Mikayel, Jiang, Minqi, Lange, Robert Tjarko, Whiteson, Shimon, Lacerda, Bruno, Hawes, Nick, Rocktaschel, Tim, Lu, Chris, Foerster, Jakob Nicolaus
Benchmarks play an important role in the development of machine learning algorithms. For example, research in reinforcement learning (RL) has been heavily influenced by available environments and benchmarks. However, RL environments are traditionally
Externí odkaz:
http://arxiv.org/abs/2311.10090