Summary of Foundational Challenges in Assuring Alignment and Safety Of Large Language Models, by Usman Anwar et al.
Foundational Challenges in Assuring Alignment and Safety of Large Language Models
by Usman Anwar, Abulhair Saparov, Javier Rando, Daniel Paleka, Miles Turpin, Peter Hase, Ekdeep Singh Lubana, Erik Jenner, Stephen Casper, Oliver Sourbut, Benjamin L. Edelman, Zhaowei Zhang, Mario Günther, Anton Korinek, Jose Hernandez-Orallo, Lewis Hammond, Eric Bigelow, Alexander Pan, Lauro Langosco, Tomasz Korbak, Heidi Zhang, Ruiqi Zhong, Seán Ó hÉigeartaigh, Gabriel Recchia, Giulio Corsi, Alan Chan, Markus Anderljung, Lilian Edwards, Aleksandar Petrov, Christian Schroeder de Witt, Sumeet Ramesh Motwan, Yoshua Bengio, Danqi Chen, Philip H.S. Torr, Samuel Albanie, Tegan Maharaj, Jakob Foerster, Florian Tramer, He He, Atoosa Kasirzadeh, Yejin Choi, David Krueger
First submitted to arxiv on: 15 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper identifies 18 fundamental challenges in ensuring the alignment and safety of large language models (LLMs). The challenges are categorized into three areas: understanding LLMs scientifically, developing and deploying them effectively, and addressing sociotechnical concerns. By highlighting these challenges, the authors pose over 200 concrete research questions that need to be addressed. This work is crucial for advancing the development and responsible deployment of LLMs, which have significant implications for various applications, including natural language processing, dialogue systems, and human-computer interaction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper finds 18 key problems with making sure large language models are safe and working correctly. These issues fall into three groups: understanding how these models work, creating and using them effectively, and dealing with social and technical challenges. The authors then come up with over 200 specific questions that need to be answered. This is important because it can help us make progress on developing and using these language models responsibly. |
Keywords
» Artificial intelligence » Alignment » Natural language processing
