title: Phi-3 Safety Post-Training: Aligning Language Models with a "Break-Fix"
    Cycle
summary: 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.
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paper_title: Phi-3 Safety Post-Training: Aligning Language Models with a "Break-Fix"
    Cycle
paper_summary: 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.
paper_ai_summary: A methodology using iterative dataset refinement and safety evaluation improves the performance of Phi-3 language models across responsible AI benchmarks.
paper_ai_keywords: "break-fix" cycle dataset curation safety post-training benchmarking red teaming vulnerability identification responsible AI benchmarks