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.
669dc1cd32408ac579905352
Emman Haider
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2024-07-22T12:19:45.850Z
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Emman Haider
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emmanhaider
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Daniel Perez-Becker
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Thomas Portet
admin_assigned
2024-07-22T12:20:27.402Z
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Thomas Portet
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thopo
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Piyush Madan
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2024-07-22T12:21:57.219Z
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Piyush Madan
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PiyushMadan
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Amit Garg
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David Majercak
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2024-07-22T12:22:29.238Z
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David Majercak
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damajercakms
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Wen Wen
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Dongwoo Kim
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2024-07-22T12:23:07.872Z
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Dongwoo Kim
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dongwookim-ms
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Ziyi Yang
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Jianwen Zhang
admin_assigned
2024-07-22T12:23:44.051Z
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Jianwen Zhang
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Jianwen
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Hiteshi Sharma
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Blake Bullwinkel
admin_assigned
2024-07-22T12:27:00.925Z
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Blake Bullwinkel
user
blakebullwinkel
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Martin Pouliot
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Amanda Minnich
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Shiven Chawla
admin_assigned
2024-07-22T12:27:23.945Z
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shiven chawla
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shivenc-microsoft
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Solianna Herrera
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Shahed Warreth
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Maggie Engler
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2024-07-22T12:27:45.024Z
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Maggie Engler
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mrengler
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Gary Lopez
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2024-07-22T12:27:51.103Z
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Gary Lopez
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Glopeztejano15
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Nina Chikanov
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Raja Sekhar Rao Dheekonda
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Bolor-Erdene Jagdagdorj
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Roman Lutz
admin_assigned
2024-07-22T12:28:18.369Z
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Roman Lutz
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Richard Lundeen
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Tori Westerhoff
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Pete Bryan
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2024-07-22T12:28:55.948Z
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Pete Bryan
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Christian Seifert
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Ram Shankar Siva Kumar
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2024-07-22T12:29:59.954Z
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Ram Shankar Siva Kumar
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ramshans
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Andrew Berkley
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Alex Kessler
admin_assigned
2024-07-22T12:29:48.124Z
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Alex Kessler
user
Kessler111
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.
A methodology using iterative dataset refinement and safety evaluation improves the performance of Phi-3 language models across responsible AI benchmarks.
"break-fix" cycle dataset curation safety post-training benchmarking red teaming vulnerability identification responsible AI benchmarks