Abstract
Foundational Challenges in Assuring Alignment and Safety of Large Language Models
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, and sociotechnical challenges. Based on the identified challenges, we pose 200+ concrete research questions.
Anwar, U., Saparov, A., Rando, J., Paleka, D., Turpin, M., Hase, P., Lubana, E. S., Jenner, E., Casper, S., Sourbut, O., Edelman, B. L., Zhang, Z., Günther, M., Korinek, A., Hernandez-Orallo, J., Hammond, L., Bigelow, E., Pan, A., Langosco, L., Korbak, T., Ó hÉigeartaigh, S., Recchia, G., Corsi, G., Chan, A., Anderljung, M., Edwards, L., Petrov, A., Schroeder de Witt, C., Motwani, S. R., Bengio, Y., & Others. (2024, September 2). Foundational challenges in assuring alignment and safety of large language models [Poster summary]. Transactions on Machine Learning Research.
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Université de Montréal
- Mila