arXiv:2606.03110v1 Announce Type: new Abstract: Aligning AI systems with diverse human values requires value specifications grounded in concrete examples, but generating such examples without extensive human supervision remains an open challenge. We investigate what makes these examples effective, using Internal Coherence Maximization (ICM) -- which infers labels by maximizing their mutual predictability -- to generate persona-specific examples that steer a model toward a target group's values, without human supervision. Across four benchmarks spanning classification, preference, and open-ende
This research addresses a critical limitation in AI alignment by proposing a novel, unsupervised method for generating value-aligned examples, becoming more relevant as AI systems permeate diverse societal applications.
This development could significantly accelerate the creation of AI systems that are more aligned with diverse human values without extensive manual input, fostering greater trust and wider adoption.
The ability to generate persona-specific, value-grounded examples without human supervision fundamentally changes the scalability and efficiency of AI alignment efforts.
- · AI developers
- · Ethical AI frameworks
- · Social science researchers
- · Organisations needing custom AI alignment
- · Companies relying on extensive human annotations for AI alignment
- · AI systems failing to adapt to diverse user values
AI models become more adaptable and trustworthy across a wider range of user groups and cultural contexts.
This improved alignment could reduce biases and unintended consequences, leading to broader societal acceptance and integration of AI systems.
The reduced cost and increased efficacy of alignment could accelerate the development and deployment of truly autonomous AI agents capable of operating effectively in complex social environments.
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Read at arXiv cs.CL