# MAP: Multi-Human-Value Alignment Palette

arXiv: https://arxiv.org/abs/2410.19198 · HTML: https://mlsanity.com/paper/2410.19198/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Placed in Human values and AI feedback alignment, one of 10 directions in LLM alignment / RLHF / preference optimisation.
- Builds on 17 of its 31 references that are in the map.
- First posted on arXiv 24 Oct 2024.

## Placed in

- LLM alignment / RLHF / preference optimisation / [Human values and AI feedback alignment](https://mlsanity.com/alignment/human-values-and-ai-feedback-alignment/)

## Builds on (most cited first, 17 of 17)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2204.05862/) (2022)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [Safe RLHF: Safe Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2310.12773/) (2023)
- [Artificial Intelligence, Values and Alignment](https://mlsanity.com/paper/2001.09768/) (2020)
- [Fine-Grained Human Feedback Gives Better Rewards for Language Model Training](https://mlsanity.com/paper/2306.01693/) (2023)
- [Rewarded soups: towards Pareto-optimal alignment by interpolating weights fine-tuned on diverse rewards](https://mlsanity.com/paper/2306.04488/) (2023)
- [A General Framework for Updating Belief Distributions](https://mlsanity.com/paper/1306.6430/) (2013)
- [Rewards-in-Context: Multi-objective Alignment of Foundation Models with Dynamic Preference Adjustment](https://mlsanity.com/paper/2402.10207/) (2024)
- [Deep Reinforcement Learning for Multi-objective Optimization](https://mlsanity.com/paper/1906.02386/) (2019)
- [Deep Reinforcement Learning from Policy-Dependent Human Feedback](https://mlsanity.com/paper/1902.04257/) (2019)
- [Assigning a value to a power likelihood in a general Bayesian model](https://mlsanity.com/paper/1701.08515/) (2017)
- [Model Selection Techniques -- An Overview](https://mlsanity.com/paper/1810.09583/) (2018)
- [Contextual Moral Value Alignment Through Context-Based Aggregation](https://mlsanity.com/paper/2403.12805/) (2024)
- [Language Model Decoding as Direct Metrics Optimization](https://mlsanity.com/paper/2310.01041/) (2023)

## Built on by (most cited first, 20 of 20)

- [Latent Thinking Optimization: Your Latent Reasoning Language Model Secretly Encodes Reward Signals in Its Latent Thoughts](https://mlsanity.com/paper/2509.26314/) (2025)
- [Probe Pruning: Accelerating LLMs through Dynamic Pruning via Model-Probing](https://mlsanity.com/paper/2502.15618/) (2025)
- [An Outlook on the Opportunities and Challenges of Multi-Agent AI Systems](https://mlsanity.com/paper/2505.18397/) (2025)
- [OrthAlign: Orthogonal Subspace Decomposition for Non-Interfering Multi-Objective Alignment](https://mlsanity.com/paper/2509.24610/) (2025)
- [Multi-Value Alignment for LLMs via Value Decorrelation and Extrapolation](https://mlsanity.com/paper/2511.17579/) (2025)
- [Towards Proactive Personalization through Profile Customization for Individual Users in Dialogues](https://mlsanity.com/paper/2512.15302/) (2025)
- [Aligning VLM Assistants with Personalized Situated Cognition](https://mlsanity.com/paper/2506.00930/) (2025)
- [VC-Soup: Value-Consistency Guided Multi-Value Alignment for Large Language Models](https://mlsanity.com/paper/2603.18113/) (2026)
- [Beyond Compromise: Pareto-Lenient Consensus for Efficient Multi-Preference LLM Alignment](https://mlsanity.com/paper/2604.05965/) (2026)
- [PEO: Improving Bi-Factorial Preference Alignment with Post-Training Policy Extrapolation](https://mlsanity.com/paper/2503.01233/) (2025)
- [Align When They Want, Complement When They Need! Human-Centered Ensembles for Adaptive Human-AI Collaboration](https://mlsanity.com/paper/2602.20104/) (2026)
- [Explaining and Breaking the Safety-Helpfulness Ceiling via Preference Dimensional Expansion](https://mlsanity.com/paper/2605.11679/) (2026)
- [DiverValue-Bench: A Benchmark and Fine-Tuning Framework for Aligning Large Language Models with Diverse Human Values](https://mlsanity.com/paper/2509.08022/) (2025)
- [Adversarial Alignment: Ensuring Value Consistency in Large Language Models for Sensitive Domains](https://mlsanity.com/paper/2601.13137/) (2026)
- [Learning the Value Systems of Societies with Preference-based Multi-objective Reinforcement Learning](https://mlsanity.com/paper/2602.08835/) (2026)
- [Granite.Trust Policy Tools: Shareable, Actionable Policies for Generative AI Applications](https://mlsanity.com/paper/2608.23870/) (2026)
- [Probing the Structure and Dynamics of LLM Value Expression through Value Conflicts](https://mlsanity.com/paper/2609.07296/) (2026)
- [An Efficient and Modular Framework for Targeted Harm Mitigation in LLMS](https://mlsanity.com/paper/2609.13624/) (2026)
- [From Alignment to Access Control: A Framework for GenAI Policy Enforcement](https://mlsanity.com/paper/2609.26682/) (2026)
- [AlphaPareto: Formulaic Alpha Discovery with LLM-Guided Multi-Objective Reinforcement Learning](https://mlsanity.com/paper/2609.34188/) (2026)

## Abstract (arXiv)

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.