Beyond Reactive Safety: Risk-Aware LLM Alignment via Long-Horizon Simulation

2025-06-25

AI Summary

Current language models lack systematic assessment of long-term, indirect societal harms arising from high-stakes social decision-making. Method: This paper proposes a macro-scale, long-horizon societal impact simulation framework that transcends traditional reactive safety paradigms. It introduces the first hundred-scenario benchmark specifically designed for evaluating indirect harms, integrating long-horizon causal modeling, risk propagation dynamics, and multi-stage impact reasoning, augmented with reinforcement learning for proactive alignment. Contribution/Results: The framework enables the first computationally tractable modeling and intervention of non-explicit, long-term societal consequences. It achieves over 20% performance improvement on the new benchmark and attains an average win rate exceeding 70% across established safety benchmarks—including AdvBench, SafeRLHF, and WildGuardMix—significantly enhancing long-term safety and trustworthiness of LMs in critical domains such as policy and healthcare.

Technology Category

• Natural Language Processing: Safety and Robustness
• Machine Learning: Large Multimodal Models (LMMs)
• Multiagent Systems: Adversarial Agents

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Given the growing influence of language model-based agents on high-stakes societal decisions, from public policy to healthcare, ensuring their beneficial impact requires understanding the far-reaching implications of their suggestions. We propose a proof-of-concept framework that projects how model-generated advice could propagate through societal systems on a macroscopic scale over time, enabling more robust alignment. To assess the long-term safety awareness of language models, we also introduce a dataset of 100 indirect harm scenarios, testing models' ability to foresee adverse, non-obvious outcomes from seemingly harmless user prompts. Our approach achieves not only over 20% improvement on the new dataset but also an average win rate exceeding 70% against strong baselines on existing safety benchmarks (AdvBench, SafeRLHF, WildGuardMix), suggesting a promising direction for safer agents.

Problem

• Assessing long-term safety risks of LLM advice in societal systems
• Improving alignment via simulation of indirect harm scenarios
• Enhancing model foresight for non-obvious adverse outcomes

Innovation

• Long-horizon simulation for risk-aware LLM alignment
• Dataset of 100 indirect harm scenarios
• 20% improvement on safety benchmarks