VALUEFLOW: Toward Pluralistic and Steerable Value-based Alignment in Large Language Models

2026-06-08 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, extended

Summary

VALUEFLOW is a unified framework designed to address persistent gaps in aligning Large Language Models with diverse human values, specifically in hierarchical structure extraction, calibrated intensity evaluation, and steerability control. It integrates HiVES, a hierarchical value embedding space that captures intra- and cross-theory value structure; VIDB, a large-scale Value Intensity DataBase with intensity estimates derived from ranking-based aggregation; and an anchor-based evaluator for consistent intensity scores. A comprehensive study across ten models and four value theories (SVT, MFT, Rights, Duties) identified asymmetries in steerability and composition laws for multi-value control. HiVES improved ranking consistency by over 20% and similarity correlation by over 50% compared to baselines. This framework establishes scalable infrastructure for evaluating and controlling value intensity, advancing pluralistic alignment of LLMs.

Key takeaway

For Machine Learning Engineers developing value-aligned LLMs, you should integrate VALUEFLOW's ranking-based evaluation and hierarchical embedding space. This framework provides calibrated intensity control and reveals asymmetric steerability, allowing you to move beyond surface-level preferences. By leveraging HiVES for value profiling and VIDB for robust intensity assessment, you can achieve more pluralistic, accountable, and reproducible alignment, improving behavior prediction accuracy by over 10% on some attributes (e.g., Phi-4 Religion 44.5%→58.9%).

Key insights

VALUEFLOW unifies LLM value extraction, evaluation, and steering with calibrated intensity control, addressing pluralistic alignment challenges.

Principles

• Human values are stable, hierarchical motivational principles.
• Ranking-based evaluation offers more stable intensity signals than ratings.
• LLM value steerability shows asymmetric dose-response behavior.

Method

VALUEFLOW constructs HiVES for hierarchical value embeddings, builds VIDB using ranking-based aggregation for intensity estimates, and employs an anchor-based evaluator for consistent intensity scores.

In practice

• Use HiVES to extract nuanced value profiles.
• Employ VIDB for stable, calibrated value intensity assessment.
• Steer LLMs with (value, intensity) pairs for pluralistic control.

Topics

• LLM Alignment
• Human Values
• Value Steerability
• Hierarchical Embeddings
• Value Intensity Database
• Ranking-based Evaluation

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Ethicist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.