Summary of Phi-3 Safety Post-training: Aligning Language Models with a “break-fix” Cycle, by Emman Haider et al.

Phi-3 Safety Post-Training: Aligning Language Models with a “Break-Fix” Cycle

by 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

First submitted to arxiv on: 18 Jul 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary This abstract presents a methodology for aligning language models with human preferences and safety considerations, crucial as these models are increasingly deployed in various domains. The “break-fix” cycle used by the authors involves multiple rounds of dataset curation, post-training safety checks, benchmarking, red teaming, and vulnerability identification to address harm areas in single- and multi-turn scenarios. The results show iterative performance improvements across a range of responsible AI benchmarks for the Phi-3 series. The paper also includes additional strategies and evaluations used to test the safety behavior of optimized models, such as Phi-3.5-mini and Phi-3.5-MoE, which feature multilingual capabilities.
Low	GrooveSquid.com (original content)	Low Difficulty Summary This paper is about making sure language models are safe and align with human values. Right now, these models can fit on a smartphone, but that’s not enough – we need to make sure they’re not causing harm. To do this, the authors used a special process called “break-fix” to check their Phi-3 series of language models for safety. They did lots of testing and tweaking until the models got better at staying safe in different situations. The results are promising, showing that these models can get safer with more work.