Foundational Challenges in Assuring Alignment and Safety of Large Language Models
Paper · 2024 · Usman Anwar, Abulhair Saparov, Javier Rando et al.
Summary
This agenda-style paper by Anwar and a large multi-institution team asks what still prevents large language models from being reliably aligned and safe. The team spans Cambridge, NYU, ETH Zurich, Oxford, Mila and others, with David Krueger as senior author. The paper introduces no new dataset or model. Instead, it surveys existing research and sorts open problems into 18 foundational challenges under three headings:
- Scientific understanding of LLMs: opaque in-context learning, hard-to-measure capabilities, poorly characterized scaling effects, and risks from agentic and multi-agent systems.
- Development and deployment methods: pretraining data, finetuning, evaluation, interpretability, jailbreaks and data poisoning.
- Sociotechnical issues: unclear target values, dual-use misuse, trustworthiness, economic disruption and governance.
From this map the authors derive more than 200 concrete research questions. The main result is a structured, broad research roadmap. It matters because it challenges the view that impactful LLM research now requires industrial-scale resources, arguing that academic groups can still contribute substantially to alignment and safety. It also positions itself as broader than company-specific safety agendas.
Key points
- The paper identifies 18 challenges in three categories and poses 200+ research questions. It was published as arXiv:2404.09932 (v2, September 2024) and reviewed on OpenReview.
- Technical topics include in-context learning (ICL), emergence and scaling laws, and limits of Transformer reasoning. They also cover LLM-agent oversight, multi-agent collusion and safety–performance trade-offs.
- Deployment-side problems include insufficient data filtering and dataset auditing, and finetuning that misgeneralizes unpredictably. Further issues are evaluation confounded by prompt sensitivity and test-set contamination, interpretability methods lacking faithfulness, and missing robust privilege levels within LLM inputs.
- Sociotechnical sections cover:
- justifying and reconciling value choices;
- misuse in cybersecurity, surveillance and biological or chemical hazards;
- effects on workforce, inequality and education;
- the need for fast-moving and international governance.
- The authors position the work against narrower predecessors, such as Kenton et al. (2021) and the agendas of LLM companies. They also share the view, argued by Ignat et al. (2023) as well as Saphra et al. (2023), that academic research remains viable.
Fields, topics and concepts
📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksSocial Sciences · 1,954 worksSafety Research · 626 worksEngineering · 188 worksSafety, Risk, Reliability and Quality · 14 works
🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 3,894 worksBias, fairness & safety · 587 works
Cite
Cite as 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 Gunther, Anton Korinek, Jose Hernandez-Orallo, Lewis Hammond, Eric Bigelow, Alexander Pan, Lauro Langosco, Tomasz Korbak, Heidi Zhang, Ruiqi Zhong, Sean O hEigeartaigh, 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 and David Krueger. 2024. Foundational Challenges in Assuring Alignment and Safety of Large Language Models. arXiv:2404.09932.
@misc{anwar2024foundational,
title = {{Foundational Challenges in Assuring Alignment and Safety of Large Language Models}},
author = {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 Gunther, Anton Korinek, Jose Hernandez-Orallo, Lewis Hammond, Eric Bigelow, Alexander Pan, Lauro Langosco, Tomasz Korbak, Heidi Zhang, Ruiqi Zhong, Sean O hEigeartaigh, 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 and David Krueger},
year = {2024},
doi = {10.48550/arxiv.2404.09932},
eprint = {2404.09932},
archivePrefix = {arXiv},
url = {https://doi.org/10.48550/arxiv.2404.09932}
}References
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