# Foundational Challenges in Assuring Alignment and Safety of Large Language Models

**Authors:** 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  
**arXiv:** [2404.09932](https://arxiv.org/abs/2404.09932)  
**Date:** 2024-04-15  
**Summary page:** https://www.summarizepaper.com/en/paper/2404.09932v1/

## Key points

- Challenges surrounding the alignment and safety of large language models (LLMs)
- Identified 18 foundational challenges in three main categories: scientific understanding of LLMs, development and deployment methods, and sociotechnical challenges
- Over $200 concrete research questions provided based on these challenges to guide future research
- Reader's guide suggests starting with the main introduction to understand the high-level context before exploring specific challenge categories
- Primary audience: technical researchers in machine learning and natural language processing with a first-year graduate student level of knowledge
- Aim is to help junior researchers or those new to LLMs identify actionable research directions
- Many challenges offer interesting technical and scientific perspectives beyond safety and alignment focus
- Sociotechnical researchers and stakeholders encouraged to explore Section 4 emphasizing sociotechnical nature of LLM systems and need for thoughtful consideration for safety
- Agenda aims to foster collaboration across disciplines to effectively address complex challenges in LLMs

## Summary

This document delves into the challenges surrounding the alignment and safety of large language models (LLMs), identifying 18 foundational challenges that fall into three main categories: scientific understanding of LLMs, development and deployment methods, and sociotechnical challenges. It presents over $200 concrete research questions based on these challenges to guide future research in this area. The reader's guide provides strategies for navigating the document efficiently, suggesting starting with the main introduction to grasp the high-level context before exploring specific challenge categories. Technical researchers in machine learning and natural language processing are the primary audience, with the content accessible to those with a first-year graduate student level of knowledge in these fields. The aim is to help junior researchers or those new to LLMs identify actionable research directions. While the focus is on safety and alignment of LLMs, many challenges identified also offer interesting technical and scientific perspectives. Sociotechnical researchers and other stakeholders are encouraged to explore Section 4, which emphasizes the sociotechnical nature of LLM systems and how their safety requires thoughtful consideration from various fields. The agenda aims to spark collaboration across disciplines to address these complex challenges effectively. Overall, this document serves as a comprehensive guide for researchers seeking promising research directions in the field of large language models, offering detailed insights into key challenges and potential avenues for future exploration.