Reasoning over Precedents Alongside Statutes: Case-Augmented Deliberative Alignment for LLM Safety

TL;DR

This paper introduces CADA, a case-augmented deliberative alignment method for LLMs that improves safety and robustness by training models on safety reasoning cases, avoiding rigid rule adherence.

Contribution

It demonstrates that case-augmented reasoning enhances safety and helpfulness in open-source LLMs, providing a practical alternative to rule-based safety approaches.

Findings

CADA improves harmlessness and robustness against attacks.

Training on safety reasoning cases enhances generalization.

CADA reduces over-refusal while maintaining utility.

Abstract

Ensuring that Large Language Models (LLMs) adhere to safety principles without refusing benign requests remains a significant challenge. While OpenAI introduces deliberative alignment (DA) to enhance the safety of its o-series models through reasoning over detailed ``code-like'' safety rules, the effectiveness of this approach in open-source LLMs, which typically lack advanced reasoning capabilities, is understudied. In this work, we systematically evaluate the impact of explicitly specifying extensive safety codes versus demonstrating them through illustrative cases. We find that referencing explicit codes inconsistently improves harmlessness and systematically degrades helpfulness, whereas training on case-augmented simple codes yields more robust and generalized safety behaviors. By guiding LLMs with case-augmented reasoning instead of extensive code-like safety rules, we avoid rigid adherence to narrowly enumerated rules and enable broader adaptability. Building on these insights, we propose CADA, a case-augmented deliberative alignment method for LLMs utilizing reinforcement learning on self-generated safety reasoning chains. CADA effectively enhances harmlessness, improves robustness against attacks, and reduces over-refusal while preserving utility across diverse benchmarks, offering a practical alternative to rule-only DA for improving safety while maintaining helpfulness.