Risk-Aware Alignment for Safer Language Models
Analysis
This paper addresses the critical issue of safety in fine-tuning language models. It moves beyond risk-neutral approaches by introducing a novel method, Risk-aware Stepwise Alignment (RSA), that explicitly considers and mitigates risks during policy optimization. This is particularly important for preventing harmful behaviors, especially those with low probability but high impact. The use of nested risk measures and stepwise alignment is a key innovation, offering both control over model shift and suppression of dangerous outputs. The theoretical analysis and experimental validation further strengthen the paper's contribution.
Key Takeaways
- •Proposes Risk-aware Stepwise Alignment (RSA) for safer language model fine-tuning.
- •RSA uses nested risk measures to explicitly address and mitigate risks.
- •The method aims to control model shift and suppress low-probability, high-impact harmful behaviors.
- •Experimental results demonstrate improved safety and helpfulness.
“RSA explicitly incorporates risk awareness into the policy optimization process by leveraging a class of nested risk measures.”