Kavli Affiliate: Yi Zhou
| First 5 Authors: Xinran Wang, Qi Le, Ammar Ahmed, Enmao Diao, Yi Zhou
| Summary:
Ensuring that generative AI systems align with human values is essential but
challenging, especially when considering multiple human values and their
potential trade-offs. Since human values can be personalized and dynamically
change over time, the desirable levels of value alignment vary across different
ethnic groups, industry sectors, and user cohorts. Within existing frameworks,
it is hard to define human values and align AI systems accordingly across
different directions simultaneously, such as harmlessness, helpfulness, and
positiveness. To address this, we develop a novel, first-principle approach
called Multi-Human-Value Alignment Palette (MAP), which navigates the alignment
across multiple human values in a structured and reliable way. MAP formulates
the alignment problem as an optimization task with user-defined constraints,
which define human value targets. It can be efficiently solved via a
primal-dual approach, which determines whether a user-defined alignment target
is achievable and how to achieve it. We conduct a detailed theoretical analysis
of MAP by quantifying the trade-offs between values, the sensitivity to
constraints, the fundamental connection between multi-value alignment and
sequential alignment, and proving that linear weighted rewards are sufficient
for multi-value alignment. Extensive experiments demonstrate MAP’s ability to
align multiple values in a principled manner while delivering strong empirical
performance across various tasks.
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