Phi-3 Safety Post-Training: Aligning Language Models with a "Break-Fix" Cycle
<|reference_start|>Phi-3 Safety Post-Training: Aligning Language Models with a "Break-Fix" Cycle: Recent innovations in language model training have demonstrated that it is possible to create highly performant models that are small enough to run on a smartphone. As these models are deployed in an increasing number of domains, it is critical to ensure that they are aligned with human preferences and safety considerations. In this report, we present our methodology for safety aligning the Phi-3 series of language models. We utilized a "break-fix" cycle, performing multiple rounds of dataset curation, safety post-training, benchmarking, red teaming, and vulnerability identification to cover a variety of harm areas in both single and multi-turn scenarios. Our results indicate that this approach iteratively improved the performance of the Phi-3 models across a wide range of responsible AI benchmarks. Finally, we include additional red teaming strategies and evaluations that were used to test the safety behavior of Phi-3.5-mini and Phi-3.5-MoE, which were optimized for multilingual capabilities.<|reference_end|>
@article{haider2024phi-3,
    title={Phi-3 Safety Post-Training: Aligning Language Models with a "Break-Fix"
  Cycle},
    author={Emman Haider, Daniel Perez-Becker, Thomas Portet, Piyush Madan, Amit
  Garg, Atabak Ashfaq, David Majercak, Wen Wen, Dongwoo Kim, Ziyi Yang, Jianwen
  Zhang, Hiteshi Sharma, Blake Bullwinkel, Martin Pouliot, Amanda Minnich,
  Shiven Chawla, Solianna Herrera, Shahed Warreth, Maggie Engler, Gary Lopez,
  Nina Chikanov, Raja Sekhar Rao Dheekonda, Bolor-Erdene Jagdagdorj, Roman
  Lutz, Richard Lundeen, Tori Westerhoff, Pete Bryan, Christian Seifert, Ram
  Shankar Siva Kumar, Andrew Berkley, Alex Kessler},
    journal={arXiv preprint arXiv:2407.13833},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.13833},
    primaryClass={cs.CL cs.AI}
}