Coherence Maximization Improves Pluralistic Alignment
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-ended generation, ICM-inferred in-context examples match the performance of gold labels. Crucially, coherence matters beyond individual label accurac...
| Authors | Taslim Mahbub, Yiding Pei, Shi Feng |
|---|---|
| Primary category | cs.CL |
| Published | 2 June 2026 |
| Last updated at source | 3 June 2026 |
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