arXiv:2602.03160v2 Announce Type: replace Abstract: Aligning Large Language Models (LLMs) with the diverse spectrum of human values remains a central challenge: preference-based methods often fail to capture deeper motivational principles. Value-based approaches offer a more principled path, yet three gaps persist: extraction often ignores hierarchical structure, evaluation detects presence but not calibrated intensity, and the steerability of LLMs at controlled intensities remains insufficiently understood. To address these limitations, we introduce VALUEFLOW, the first unified framework that
The increasing sophistication of LLMs and growing concerns about their alignment with human values necessitates more robust and steerable methods beyond simple preference modeling.
Achieving pluralistic and steerable value alignment is critical for the safe, ethical, and effective deployment of powerful AI systems across diverse applications and cultures.
This framework offers a principled pathway to integrate complex human values into LLMs, potentially leading to more reliable and trustworthy AI agents.
- · AI developers
- · Ethical AI researchers
- · Organizations deploying LLMs
- · LLMs with superficial alignment methods
- · Ethical frameworks lacking granularity
Improved trust and adoption of advanced LLMs in sensitive applications.
Development of customized LLM 'personalities' or 'ethics' for specific user groups or industries.
New regulatory frameworks and certification processes based on quantifiable value alignment metrics.
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Read at arXiv cs.AI