Safer-Instruct: Aligning Language Models with Automated Preference Data

Automated preference data can reduce annotation burden: Safer-Instruct generates large-scale preference data without human annotators, addressing the resource-intensive nature of traditional RLHF annotation processes that require significant time and creative effort.

Synthetic safety data outperformed human-annotated alternatives: Models fine-tuned on Safer-Instruct's automatically generated safety preference dataset demonstrated improved harmlessness compared to models trained on human-annotated safety preference data, suggesting automated methods can match or exceed human-generated training data quality.

Performance maintained across downstream tasks: The approach achieved improved safety outcomes while maintaining competitive performance on other language model tasks, indicating that focusing on safety through this method doesn't compromise general model capabilities.

Framework extends beyond safety applications: While demonstrated as a safety case study, Safer-Instruct's pipeline using reversed instruction tuning, instruction induction, and expert model evaluation is designed to generate preference data across various domains, potentially accelerating responsible AI development across multiple problem areas.

Addresses scalability challenges in AI alignment: By automating preference data generation, this work tackles a key bottleneck in developing aligned AI systems—the scarcity of high-quality preference annotations—making it more feasible to scale responsible AI practices across different applications and organizations.