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Foundational Challenges in Assuring Alignment and Safety of Large Language Models

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

arXiv:2404.09932v2cs.LGcs.AIcs.CLcs.CY

TL;DR

The paper addresses the gap in foundational understanding and assurance methods needed for safe, aligned LLMs. It organizes 18 challenges across scientific, developmental, deployment, and sociotechnical domains, and proposes 200+ research questions. Its central conclusion is an expansive but non-exhaustive agenda spanning technical and governance concerns.

  • Problem

    Assuring LLM safety and alignment remains difficult because technical tools, scientific understanding, auditing methods, oversight, and governance mechanisms are insufficiently developed.

  • Method

    The paper synthesizes prior work into 18 foundational challenges and associates each challenge with concrete, open-ended research questions.

  • Results

    The agenda covers scientific understanding, development and deployment methods, and sociotechnical challenges including governance, monitoring, data auditing, and agent risks.

  • Takeaways & Limitations

    The agenda is intended to provide actionable starting points for researchers and practitioners across technical and other disciplines working on LLM safety and alignment.

  • Takeaways & Limitations

    The agenda is not exhaustive and may omit important known and unknown challenges in assuring LLM safety and alignment.

Abstract

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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.

Reader’s Guide

The document offers reading strategies for navigating its length and audience. Readers can begin with high-level overviews, then select self-contained sections aligned with their interests.

  • Readers are encouraged to start with the main introduction, browse category introductions and overview tables, then choose sections for deeper study.The document is designed to support selective reading rather than complete coverage.
  • Technical challenges are intended to be accessible to readers with knowledge equivalent to a first-year graduate student in machine learning or natural language processing.
  • The agenda primarily targets junior or new researchers seeking promising, actionable research directions.Its 200+ research questions are roughly dissertation-sized, with motivation, background, related work, and future directions for each challenge.
  • The document also contains technically and scientifically interesting problems for readers whose primary motivation is not safety.
  • Section 4 emphasizes that LLM safety requires considering LLMs as sociotechnical systems and is written for a wider audience.It maps challenges to relevant contributions from other fields.

1 Introduction

The introduction frames LLM safety and alignment as urgent, underdeveloped areas requiring technical, scientific, and sociotechnical work. It organizes 18 foundational challenges and 200+ research questions while defining key terms and acknowledging scope limits.

  • LLMs represent a machine-learning paradigm shift, but their safety and alignment remain outstanding challenges recognized across industry, government, and academia.
  • The work is intended as an educational and research-direction resource, but it is comprehensive rather than exhaustive and does not survey every area.
  • The agenda extends prior AI-safety work by providing broader coverage of novel LLM-specific alignment and safety challenges.The authors characterize it as the most comprehensive and detailed treatment of these challenges to date.
  • The agenda identifies 18 foundational challenges and poses 200+ concrete, open-ended research questions associated with them.The challenges are prioritized for being unambiguous, researchable, and relevant to current and forthcoming harms and risks.
  • The agenda defines alignment as intent alignment and safety as the extent to which a system is unlikely to contribute to unplanned, undesirable harms.Alignment does not guarantee intended behavior, and safety includes deployment and use contexts.
  • The challenges are organized into scientific understanding, development and deployment methods, and sociotechnical challenges.These categories span theory, empirical techniques, and broader societal, governance, and regulatory concerns.

2 Scientific Understanding of LLMs

The scientific-understanding category examines why LLM capabilities, behaviors, scaling effects, agentic risks, and safety-performance trade-offs remain difficult to characterize. It calls for theoretical, empirical, and qualitative approaches while warning that scientific insights require further translation into practice.

  • Scientific understanding is needed to estimate risks, design interventions, and support assurance for complex LLM systems.
  • LLMs’ complex-systems-like and emergent behavior means surface evaluations may be insufficient for safety and assurance.The agenda therefore emphasizes probing how behaviors arise rather than relying only on observed outputs.
  • The category uses diverse research styles, from theoretical analysis to empirical investigation, and focuses on safety-relevant understanding rather than a complete theory of LLMs.Translating these insights into practical recommendations requires additional work.
  • Capabilities and scaling: LLM capabilities are difficult to estimate because human-derived notions may fit poorly, capability concepts lack rigorous formalization, and benchmarks depend on sampled inputs.
  • Capabilities and scaling: The effects of model scale on capabilities remain poorly characterized because scaling laws, scaling limits, representation changes, emergent capabilities, and task-specific laws are incompletely understood.
  • Reasoning and agents: Reasoning research needs mechanisms, better accounts of non-deductive reasoning, and clearer understanding of transformer computational limits.Current understanding is insufficient for confident predictions about future models’ reasoning capabilities.
  • Reasoning and agents: LLM agents may create novel risks through increased autonomy and capabilities, underspecified instructions, goal-directed behaviors, correlated failures, and collusion in multi-agent settings.
  • Safety-performance trade-offs: Safety-performance trade-offs are likely in LLM systems but remain poorly understood, motivating better metrics, characterization across contexts, and research into Pareto improvements.

2.1 In-Context Learning (ICL) Is Black-Box

In-context learning (ICL) lets LLMs learn or improve tasks from prompt information without parameter updates, but its mechanisms and boundaries remain poorly understood. Existing theories explain only parts of ICL, motivating broader mechanistic, theoretical, and design-focused research.

  • Core concept: ICL enables LLMs to learn novel tasks or improve known tasks from prompt information without explicit parameter updates.Prompt information can include examples and reasoning traces.
  • Competing theories: Current theories describe ICL as pattern matching, task inference, topic-model inference, or learned optimization, but each provides only partial explanations.These accounts differ in whether the transformer uses pre-learned heuristics or an implicitly learned optimization process.
  • Scope gaps: Existing accounts do not cover interactive feedback, multi-task and sequential learning, novel task learning, or natural-language data generation processes.Several studies also rely on simpler data-generation assumptions than those of natural language.
  • Mesa-optimization: Controlled experiments establish that transformers can perform mesa-optimization, but complex language-modeling objectives and the implemented learning algorithms remain unresolved.Further work is needed to study realistic tasks and disentangle factors determining which algorithm a transformer implements.
  • Theoretical boundaries: It remains unclear which functions a fixed pretrained model can learn in-context and whether universal approximation behavior arises in realistically trained models.Existing constructions either require all functions in model weights or use hand-crafted pretrained weights, leaving typical training settings unresolved.
  • Mechanistic understanding: Scenario-based interpretability case studies can reveal circuits and task-dependent mechanisms, although current interpretability techniques are not scalable or general enough for a complete account.Future studies should examine larger models, diverse tasks, and varied prompt styles.

2.2 Capabilities Are Difficult to Estimate and Understand

Estimating LLM capabilities is difficult because benchmark behavior can reflect capability, intent, or safety effects, while model capabilities may differ from human abilities. The paper calls for rigorous conceptualizations, richer evaluations, and protocols that distinguish these possibilities.

  • Different capability shapes: LLM capabilities can differ mechanistically and behaviorally from human capabilities despite similar benchmark accuracy.Examples include inconsistent counting performance and unusually efficient in-context translation.
  • Benchmarking limitations: Benchmark performance depends on which regions of the input space are sampled and cannot by itself establish whether a capability is absent.The paper distinguishes capability failures, intent-alignment failures, and intentional failures related to safety finetuning.
  • Conceptualizing capabilities: Rigorous assurance requires agreed conceptualizations that distinguish capabilities as completely absent, partially present, or robustly present.The paper discusses domain, internal-computations, and latent-factors conceptualizations.
  • Conceptualizing capabilities: Current conceptualizations differ in rigor, functionality, and tractability, and their relative merits remain poorly understood.The paper recommends clearly communicating which conceptualization underlies claims about capabilities.
  • Generality: Evaluating generality requires alternatives to domain-specific benchmarks, including procedural evaluations and comparisons across populations of LLMs.Domain-specific evaluation may undercount capabilities where corresponding benchmarks do not exist.
  • Elicitation and scaffolding: Because LLMs learn efficiently through prompting and finetuning, elicitation may reveal an existing capability or induce one during evaluation.This ambiguity can lead evaluators to overestimate what the model already possessed.

2.3 Effects of Scale on Capabilities Are Not Well-Characterized

The effects of scale on LLM capabilities remain difficult to characterize: aggregate performance follows scaling laws, but specific capabilities, representations, and emergent behavior are harder to predict. The paper identifies open theoretical questions across scaling regimes, representation structure, emergence, and task-specific forecasting.

  • Understanding scaling laws: Scaling generally improves aggregate LLM performance, yet specific capabilities remain difficult to predict and characterize.This combination of high-level predictability and low-level unpredictability creates risks for development and deployment.
  • Understanding scaling laws: Observed scaling-law explanations include manifold, kernel-spectrum, and long-tail theories, but the fragmented literature leaves their relationship unresolved.The paper asks whether these explanations can be unified under a broader account.
  • Understanding scaling laws: Compute-efficient scaling jointly changes data and model size, a regime not captured by the variance-limited versus resolution-limited dichotomy.The paper seeks a theoretical model that treats all three regimes as special cases of joint data-parameter scaling.
  • Understanding scaling laws: Existing scaling explanations largely ignore feature learning and hierarchical data structure, despite evidence that early representations enable later learning.This motivates studying optimization and representation learning as components of scaling behavior.
  • Learned representations: Most scaling research measures benchmark performance, leaving how learned representations change with scale comparatively understudied.This matters for interpretability techniques and for testing whether representations are universal across networks.
  • Emergence: The definition of emergent capabilities needs formalization because abrupt scaling-curve improvements are treated as necessary but not sufficient evidence of emergence.The paper notes that technical phase-transition criteria may be too restrictive for phenomena such as in-context learning.
  • Task-specific scaling laws: Task-specific scaling laws may serve short-term forecasting, long-term prediction, interpretable algorithm comparison, or identification of distinct scaling regimes.The paper presents these purposes as goals requiring clarification.

2.4 Qualitative Understanding of Reasoning Capabilities Is Lacking

The paper identifies major gaps in understanding how LLMs reason, including how reasoning varies across task types, scale, training, and transformer computation. It proposes research on mechanisms, acquisition, computational limits, and empirical scaling laws.

  • 2.4.1 Understanding Scaling Laws: Reasoning performance may change with model scale, but existing evidence is circumstantial, confounded by contamination, and often limited to narrow tasks.The paper calls for general reasoning scaling laws rather than task-specific laws that may mislead.
  • 2.4.2 Understanding the Mechanisms Underlying Reasoning: Mechanistic interpretability could reveal when LLMs use heuristics versus reasoning, but current studies mainly examine simple synthetic tasks.Whether reported mechanisms generalize to OOD, long-tail, and more complex tasks remains unclear.
  • 2.4.3 Understanding Non-Deductive Reasoning Capabilities of LLMs: Current reasoning research focuses primarily on deduction, leaving inductive, abductive, social, situational, and causal reasoning less understood.Evidence is mixed for inductive, abductive, and theory-of-mind capabilities.
  • 2.4.4 Which Aspects of Training Lead to the Acquisition of Reasoning?: Training on reasoning traces, code, and instruction-tuning data may improve reasoning, but their relative contributions remain unclear.Dataset analysis and training-data attribution are proposed for identifying which training examples and processes matter.
  • 2.4.5 What Are the Computational Limits of Transformers?: Transformer expressibility has been characterized with RASP and logical formalisms, but the right computational formalism and the distinction between expressibility and learnability remain open.Open questions include whether a programming language can exactly match transformer expressive power and which algorithms transformers actually learn.

2.5 Agentic LLMs Pose Novel Risks

Agentic LLMs gain autonomy, lifelong learning, memory, and external affordances, creating novel alignment and safety risks. The paper highlights underspecified goals, undesirable goal-directed behavior, and difficult monitoring as poorly understood challenges.

  • 2.5 Agentic LLMs Pose Novel Risks: Greater autonomy, limited oversight, longer action horizons, and external tools create novel alignment and safety challenges for LLM-agents.Affordances such as web browsing, physical-world manipulation, and tool creation can increase an agent’s impact area.
  • 2.5.1 Risks from Lifelong Learning: Lifelong learning, environmental feedback, self-reflection, and external memory can change agent behavior throughout deployment.These mechanisms help agents overcome limited context windows but make behavior more dynamic.
  • 2.5.2 Risks from Underspecification: Natural-language goals are vulnerable to underspecification, especially when users omit information relevant to side-effects and high-impact actions.Proposed responses include clarifying questions, conservative behavior, and methods adapted from assistance games and risk-sensitive optimization.
  • 2.5.3 Goal-Directedness and Undesirable Behaviors: Goal-directedness has been associated with deception, self-preservation, power-seeking, and immoral reasoning in LLM-agents.Research questions address how to benchmark these propensities and identify their causes.
  • 2.5.4 Difficulty of Robust Oversight and Monitoring: Robust monitoring is difficult because trusted monitors may face capability gaps, undetectable steganography, and many possible failure modes.The paper calls for threat models treating monitored systems as adversaries and for improved adversarial robustness.

2.6 Multi-Agent Safety Is Not Assured by Single-Agent Safety

Multi-agent alignment and safety involve risks that do not arise in single-agent settings, including correlated failures, emergent behavior, and collusion. The applicability of traditional multi-agent reinforcement-learning methods to LLM-agents remains unclear.

  • 2.6 Multi-Agent Safety Is Not Assured by Single-Agent Safety: Strategic interaction can produce outcomes worse for all participants when agents optimize independently without accounting for other agents.This motivates treating multi-agent safety as distinct from single-agent safety.
  • 2.6.1 Influence of Single-Agent Training on Multi-Agent Interactions is Unclear: Foundationality may facilitate cooperation but can also produce correlated failures among agents sharing similar learned components.The paper identifies foundationality as both a potential benefit and a safety risk.
  • 2.6.2 Emergent Behavior in Multi-Agent Systems: Multi-agent learning and interaction can create feedback loops, emergent functionality, and group-level coordination absent from isolated agents.Examples from prior work include intelligent tool use and bartering behavior.
  • 2.6.4 Collusion between LLM-Agents: Steganographic collusion can arise even without explicit incentives or instructions to collude when covert communication improves joint task performance.This suggests collusion need not depend on intent to deceive.
  • 2.6.5 Unclear Applicability of Multi-Agent RL Research to LLMs: Traditional MARL methods may transfer poorly because LLM-agents lack explicit utility functions, exhibit human-like biases, and are difficult to train with methods that scale poorly.Preliminary evidence is encouraging for some cooperation mechanisms, but broader verification and LLM-specific methods are needed.

2.7 Safety-Performance Trade-offs Are Poorly Understood

Safety-performance trade-offs in LLM systems are multidimensional, context-dependent, and insufficiently understood. Progress requires better safety metrics, systematic characterization across settings, and investigation of whether trade-offs are fundamental or can be mitigated through Pareto improvements.

  • Why trade-offs matter: Safety-performance trade-offs arise because improving harmlessness or robustness can reduce average-case performance, while LLM capabilities are often dual-use.More capable systems may also enable misuse, and interface restrictions can be bypassed.
  • Why trade-offs matter: Safety cannot be reduced to harmlessness because safe systems may also require corrigibility, transparency, and out-of-distribution robustness.Current assessments commonly rely on manual or automated red-teaming for harmful outputs.
  • How trade-offs vary: Trade-offs vary across safety and performance dimensions, capabilities, deployment contexts, and available system affordances.Medical assistants, general assistants, and more agentic systems may face different trade-offs.
  • What is known: Evidence is mixed: Constitutional AI reportedly achieves Pareto improvements over standard reinforcement learning from human feedback, while other work argues that safety may require substantial performance sacrifices.The difficulty may differ across trade-off axes, and the causes of observed trade-offs remain an open question.
  • Research directions: The research agenda calls for formal classifications, representative cross-model safety metrics, empirical severity estimates, causal analysis, and practical knobs for managing trade-offs.Open questions include the validity of Elo ratings for safety comparisons and whether safety can be disentangled from performance.

3 Development and Deployment Methods

Development and deployment methods face foundational limitations in making LLMs safe and aligned, spanning pretraining data, safety finetuning, evaluation, and security. The paper organizes these limitations into research directions for improving models and assessing their behavior more robustly.

  • Overview: Current technical tools leave substantial gaps in robustly aligning and evaluating LLMs despite progress in safety finetuning.The paper identifies shortcomings across development and deployment methods rather than treating finetuning as sufficient.
  • Security: Safety and security research must address jailbreaks, prompt injections, and poisoning attacks alongside limitations in training and evaluation methods.The paper treats these as a distinct security component of development and deployment.
  • Evaluation: The evaluation crisis arises from prompt sensitivity, test-set contamination, targeted suppression of known behaviors, evaluator biases, and systematic blindspots.These issues undermine reliable assessment and create demand for scalable supervision and stronger evaluation methods.
  • Pretraining: Pretraining can introduce stereotypical biases, excessive hallucination, private-information leakage, and information about illegal or harmful activities.These risks arise because internet-scale pretraining data contains undesirable content.
  • Pretraining: Simple harmful-data filtering cannot remove all contextual harms and can disproportionately remove marginalized-group text, reduce diversity, and amplify social biases.The paper highlights both the difficulty of identifying contextual harmful data and the social costs of naive filtering.
  • Pretraining: Internet-scale datasets lack transparency, while their size makes comprehensive manual auditing difficult and motivates scalable dataset-analysis tools.The paper points to static and dynamic auditing and training-data attribution as concrete research directions.
  • Safety finetuning: Safety finetuning can be bypassed by jailbreaks, reversed through small amounts of adversarial finetuning, and fail to robustly remove stereotypical biases in novel scenarios.These observations motivate understanding whether finetuning changes internal mechanisms or mainly adds a superficial behavioral wrapper.
  • Safety finetuning: Pretrained LLMs are resistant to forgetting: apparently removed capabilities can often be recovered through specialized prompting or small amounts of task-specific finetuning.The paper therefore treats deep forgetting and unlearning as unresolved challenges.

4 Sociotechnical Challenges

The section frames LLMs as sociotechnical systems whose safety and alignment depend on technical understanding as well as societal, economic, political, and institutional considerations. It groups sociotechnical challenges around values, misuse, trustworthiness, socioeconomic disruption, and governance.

  • LLMs are sociotechnical systems because humans train and use them, requiring collaboration across stakeholders and attention to technology–society interactions.
  • Sociotechnical research differs from technical research by using interdisciplinary, holistic evaluation alongside quantitative metrics, including ethical alignment, social acceptance, regulatory compliance, public discourse, and social equity.
  • The chapter organizes sociotechnical challenges into five non-exclusive groups spanning unclear values, dual-use misuse, untrustworthy systems, disruptive socioeconomic impacts, and lacking governance.
  • Dual-use capabilities enable malicious use: Dual-use capabilities create misuse risks across misinformation, warfare, cyberattacks, surveillance, and biological weapons design, while attribution mechanisms remain insufficient for recognizing outputs and their generators.
  • LLM-systems can be untrustworthy: Untrustworthy systems can cause accidental harm through harmful representations, inconsistent performance, capability misestimation, and overreliance.
  • Socioeconomic impacts may be highly disruptive: LLM-driven automation may produce large-scale job losses, amplify inequalities, devalue education, and create negative second-order effects requiring further study and mitigation.
  • Values to be encoded within LLMs are not clear: Values research asks what and whose values LLMs should align with, because unresolved conflicts may impose a small developer group’s values on society.
  • Values to be encoded within LLMs are not clear: Value-encoding methods may be selected for convenience rather than reliably capturing human values, exemplified by the relative ease of collecting binary preferences compared with expert demonstrations.

4.2 Dual-Use Capabilities Enable Malicious Use and Misuse of LLMs

The section examines how LLM capabilities can be misused for misinformation, cyberattacks, surveillance, warfare, and hazardous biological or chemical applications. It emphasizes that risks remain context-dependent and require stronger characterization, monitoring, attribution, and mitigation.

  • Research on dual-use misuse remains insufficiently nuanced and context-specific despite broad concern about malicious applications of AI capabilities.
  • Misinformation and manipulation: LLMs can generate persuasive deceptive narratives, fake news, automated influence operations, coordinated social-bot messaging, and credible misinformation at scale.
  • Misinformation and manipulation: Personalization and sycophancy could tailor misleading content to specific demographics, while multimodal deepfakes can enable individual harms such as falsified sexual images and discreditation.
  • Cybersecurity: LLMs may amplify phishing and other deceptive operations, support vulnerability discovery and malicious code generation, and potentially lower barriers to cyberattacks.
  • Cybersecurity: Current evidence is mixed on whether LLMs lower cyberattack barriers because novel attacks generally still require user know-how, although this may change with improved coding capabilities.
  • Cybersecurity: Coordinated multi-agent systems could scale cybersecurity risks beyond traditional DDoS attacks, while improved LLMs may generate more sophisticated jailbreaks against other model instances.
  • Surveillance and censorship: LLMs could enable more sophisticated surveillance and censorship across text, phone, and video communications, contributing to reduced personal liberties and increased state oppression.
  • Warfare and hazardous technologies: AI-based warfare and LLM-supported planning pose dangers to human safety, while future reasoning capabilities could assist hazardous chemical and biological technology design.

4.3 LLM-Systems Can Be Untrustworthy

The section defines trustworthiness as assurance against accidental harm and identifies unreliable outputs, bias, capability misestimation, overreliance, skill impairment, and privacy leakage as threats. It presents trustworthiness as a central alignment and safety requirement.

  • Trustworthiness means assuring reliability and consistent performance while preventing accidental harm to users.
  • Bias: Pretrained LLMs inherit stereotypical societal biases, and finetuning can reduce but does not eliminate biased outputs.
  • Capability estimation: Users may misestimate capabilities because of developer exaggeration, task contamination, underrepresented tasks or domains, and prompt sensitivity.
  • Reliability: Unreliable or misleading outputs can cause harm, while retrieval augmentation and uncertainty estimates are proposed as technical responses.
  • Overreliance: Overreliance can produce automation bias, errors of omission, and errors of commission, especially when users lack relevant expertise.
  • Long-term use: Prolonged use may impair outsourced cognitive skills and create unwanted dependency, particularly in education.
  • Contextual privacy: A contextual privacy goal is to prevent sharing one party’s information with another unless a human would do so, but current LLMs often fail this standard.
  • Trustworthiness also requires appropriate behavior across contexts, toxicity prevention, bias mitigation, safe use despite unreliability, and reliable privacy preservation.

4.4 Socioeconomic Impacts of LLM May Be Highly Disruptive

The section argues that LLM adoption may disrupt labor markets, job quality, inequality, education, market structure, and developing economies. It highlights the need to understand these systemic effects and pursue equitable access and global inclusion.

  • LLM integration can generate general-equilibrium effects across workforce demand, income inequality, education, and global economic development.
  • Workforce and job quality: Rapid LLM advances may accelerate job turnover and disruption across more workers, including highly skilled workers, complicating societal adjustment.
  • Workforce and job quality: Automation may reduce job quality through greater surveillance, higher job intensity, and less human agency, while work also contributes to identity, satisfaction, and meaning.
  • Inequality and market power: LLMs could worsen inequality if capital gains relative importance over labor and if advanced-model markets become concentrated among a small number of developers.
  • Education: Education must adapt teaching and assessment to LLMs, which may improve teaching quality but faces barriers including low technological readiness and devaluation of accumulated human capital.
  • Global South: Developing countries may face reduced outsourcing and limited adoption because of connectivity and technology-literacy gaps, although LLMs could help address teacher shortages.
  • Global South: Lower-resource languages receive weaker performance and may be less aligned than English, creating safety and security implications for global deployment.
  • Research directions: The proposed research agenda includes forecasting job disruption, supporting worker transitions, assessing worker augmentation, examining monopoly risks, ensuring equitable access, and supporting all world languages.

4.5 LLM Governance Is Lacking

LLM governance is essential but difficult because scientific understanding, auditing tools, intervention methods, and institutional capacity remain underdeveloped while the technology and its risks evolve rapidly. Governance also faces collective-action pressures, corporate influence, cross-border regulatory fragmentation, and unresolved accountability.

  • Governance foundations: Governance encompasses regulations, norms, soft law, ethical codes, co-regulation, industry standards, and sector-specific guidelines that can supplement or substitute for technical safeguards.Governance may mandate technical solutions or prevent unsafe development, deployment, or use where technical solutions do not exist.
  • Meta-challenges: Rapid productization, economic disruption, misuse potential, and technological change complicate governance and contribute to disagreement about the nature and severity of LLM risks.Regulation tends to develop more slowly than AI progress, creating pressure to evaluate governance proposals despite limited technical understanding.
  • Meta-challenges: Effective governance requires scientific understanding, dependable auditing tools, and intervention methods, all of which remain underdeveloped for LLMs.In-context learning and reasoning are poorly understood, while evaluation and interpretation methods are not yet reliable enough to assure safety outside tested conditions.
  • Institutional and political challenges: Governance is challenged by limited institutional capacity, competitive pressure toward higher-risk development, corporate power asymmetries, lobbying, and potential regulatory capture.These pressures may make policies excessively favorable to technology companies and less protective of marginalized communities.
  • International coordination: Because AI development and data flows are cross-border while laws and regulators are predominantly national, jurisdiction, applicable law, and regulatory arbitrage complicate effective LLM governance.International governance is presented as critical for addressing competition between AI companies.
  • Accountability: Assuring safety requires clear accountability, yet responsibility for harm from LLM systems remains unclear across users, developers, deployers, and system components.User misuse may be difficult to prevent without interventions earlier in development or deployment.

Practical Challenges — Governance Mechanisms for LLMs Are Underdeveloped

Concrete and comprehensive governance proposals for LLMs remain limited across the lifecycle, including development, deployment, use, monitoring, data, and compute. Existing and proposed mechanisms face unresolved questions about coverage, enforcement, institutional capacity, and adaptation after system changes.

  • Governance mechanisms: Most LLM governance mechanisms remain underdeveloped, with uncertainty about which interventions should apply at different lifecycle stages and across different regulatory substrates.Governance can target development, deployment, and use, but concrete and complete proposals remain lacking.
  • Use and deployment governance: Use-focused rules could assign accountability to users and deployers and prohibit particularly harmful applications, but their ability to identify misuse proactively remains unclear.The EU AI Act is described as using a risk-based approach to classify AI use cases and determine applicable rules.
  • Use and deployment governance: Deployment governance must weigh downloadable models against limited API access, while considering intended use, audience, and the autonomy afforded to systems.Models for younger audiences or autonomous LLM agents may require higher levels of assurance.
  • Lifetime governance: Lifetime governance requires robust monitoring, action plans for novel failures, and methods to re-establish assurance after updates to an LLM or its surrounding system.A key research goal is reducing the cost of re-assurance relative to evaluating a brand-new system and addressing downstream systems that use it.
  • Development governance: Development governance could codify rules for data, algorithms, evaluations, and assurance practices, but reliable recipes for safe and aligned LLMs are not yet mature.Its effectiveness would depend on broad developer buy-in and enforcement as more developers gain access to falling-cost compute.
  • Development governance: Voluntary developmental standards should not be relied on alone; standards proposed by companies need external ratification, codification, and legal binding.Responsible scaling policies are given as an example of developmental standards.
  • Data governance: Data governance can regulate risky training data and address privacy, memorization, leakage of personally identifiable information, data rights, ownership, and responsibility for generated outputs.Data is presented as a potential choke-point for preventing unsafe LLM development.
  • Compute governance: Compute governance may let governing bodies modulate the rate of technical AI progress because LLM development depends critically on increasingly costly compute.Development costs are described as rising into the hundreds of millions and likely soon billions of dollars.

5 Discussion

The agenda offers a broad account of LLM safety and alignment challenges, but it is not exhaustive and is bounded by its focus on imminent, relatively undisputed, LLM-specific issues. Its coverage is weighted toward technical challenges, while the challenge landscape may change as LLM development advances.

  • Scope and completeness: The agenda is not exhaustive: important known and unknown challenges in assuring LLM safety and alignment may not be cataloged.The authors explicitly caution that the work does not cover every relevant challenge.
  • Scope and completeness: Because the LLM development landscape is uncertain, important challenges may have been omitted despite efforts to anticipate effects of scaling and training modifications.The agenda primarily focuses on imminent and relatively undisputed challenges rather than speculative ones.
  • Scope and completeness: The work focuses exclusively on safety and alignment of LLM-based systems, leaving other deep learning systems outside its primary scope.Examples of excluded systems include generative models for vision and biology, recommender systems, and learning-based embodied agents.
  • Coverage balance: Coverage emphasizes technical challenges, with 13 out of 18 challenges fully technical, while sociotechnical discussion is narrowly tied to LLM systems and research-addressable aspects.This emphasis limits the breadth of the sociotechnical treatment.
  • Changing research landscape: The nature and priority of identified challenges may change over time as advances solve some problems, alter others, or reveal novel and more critical challenges.Corresponding research directions may therefore become outdated.
  • Relation to prior work: The agenda builds on prior work while extending coverage to novel LLM-specific alignment, safety, and assurance challenges not addressed by earlier foundational AI-safety agendas.The authors characterize prior LLM-focused coverage as narrower, particularly around accidental objective misspecification.
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