Model Merging and Safety Alignment: One Bad Model Spoils the Bunch
Authors: name: Hasan Abed Al Kader Hammoud, name: Umberto Michieli, name: Fabio Pizzati, name: Philip Torr, name: Adel Bibi, name: Bernard Ghanem, name: Mete Ozay

Merging Large Language Models (LLMs) is a cost-effective technique for
combining multiple expert LLMs into a single versatile model, retaining the
expertise of the original ones. However, current approaches often overlook the
importance of safety alignment during merging, leading to highly misaligned
models. This work investigates the effects of model merging on alignment. We
evaluate several popular model merging techniques, demonstrating that existing
methods do not only transfer domain expertise but also propagate misalignment.
We propose a simple two-step approach to address this problem: (i) generating
synthetic safety and domain-specific data, and (ii) incorporating these
generated data into the optimization process of existing data-aware model
merging techniques. This allows us to treat alignment as a skill that can be
maximized in the resulting merged LLM. Our experiments illustrate the
effectiveness of integrating alignment-related data during merging, resulting
in models that excel in both domain expertise and alignment.