Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

MAP: Multi-Human-Value Alignment Palette

Xinran Wang, Qi Le, Ammar Ahmed, Enmao Diao, Yi Zhou, Nathalie Baracaldo, Jie Ding, Ali Anwar

TL;DR

This work develops 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 and proves that linear weighted rewards are sufficient for multi-value alignment.

Abstract

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.

MAP: Multi-Human-Value Alignment Palette

TL;DR

This work develops 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 and proves that linear weighted rewards are sufficient for multi-value alignment.

Abstract

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.

Paper Structure

This paper contains 18 sections, 5 theorems, 18 equations, 10 figures, 4 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. MAP: Multi-Human-Value Alignment Palette
  3. Problem Formulation
  4. Realizable Value Levels of the MAP problem and Pareto Frontier
  5. Computational Solution to MAP through A Primal-Dual Approach
  6. Simultaneous Versus Sequential or Greedy Alignment
  7. Experimental study
  8. Experimental Setup
  9. Effectiveness of MAP for Simultaneous Multi-value Alignment
  10. Ablation Study with Larger Model
  11. Simultaneous Versus Sequential Alignment
  12. Comparison with Other Baseline Approaches
  13. Conclusion
  14. Additional Experimental Results
  15. Hyperparameters in Data Generation and Model Training
  16. ...and 3 more sections

Key Result

Theorem 1

The solution to the MAP problem (eq_MAP_b) is where $\bm \lambda^{ \mathrm{ T} } \bm r(x,y) = \sum_{i=1}^m \lambda_i r_i(x,y)$, for some $\bm \lambda \geq \bm 0$. Moreover, assuming that $\bm r(x,y)$ is not trivially a constant on the support set of $x,y$, the above $\bm \lambda$ is the unique solution to the problem: where $Z(\bm \lambda) \overset{\Delta}{=} \mathbb{E}_{x \sim \mathcal{D}, y \

Figures (10)

  • Figure 1: Expected reward (realized value level) of generated content having Harmlessness (left) and Humor (right) versus Helpfulness for various models aligned from the Llama2-7B-chat model touvron2023llama. Each blue dot represents the expected rewards $\mathbb{E}_{x \sim \mathcal{D}, y \sim p(\cdot \mid x)} r(x, y)$ with $r$ trained from Anthropic Harmless preference data ($r_{\textrm{Harmlessness}}$) yang2024rewards, Helpfulness preference data ($r_{\textrm{Helpfulness}}$) yang2024rewards, and Humor classifier ($r_{\textrm{Humor}}$) humor_no_humor_2024. The expected rewards are numerically obtained by solving (\ref{['eq_RLHF']}) with $R = \lambda_1 r_{\textrm{Harmlessness}} + \lambda_2 r_{\textrm{Helpfulness}} + \lambda_3 r_{\textrm{Humor}}$, where $\lambda_1,\lambda_2,\lambda_3 \geq 0$ are randomly generated, and quantile-transformed to the scale of $0$ to $1$. Arrows indicate the transition from the original model to aligned models, either using the proposed approach (MAP) or a single reward function.
  • Figure 2: Randomly sampled $\bm \lambda$ that represent all the possible $\bm \lambda$ whose $\ell_1$-norm is less than 6 and its subset of all the desirable $\bm \lambda$ in aligning the OPT-1.3B model towards (a) two values: Helpfulness and Harmlessness, (b) three values: adding Humor, and (c) the same three values visualized in 3D. A desirable $\bm \lambda$ means it produces Pareto improvement over all the values. The sampling procedure for $\bm \lambda$ is the same as outlined in Section \ref{['subsec_exp_comparison']}.
  • Figure 3: Distribution of reward scores before and after aligning the Llama2-7B-chat model towards three values: Humor (left plot), Helpfulness (middle plot), and Harmlessness (right plot), using the proposed MAP. This alignment involves a user-specified palette designed to shift the expected rewards, also referred to as realized value levels, toward the 80% quantile of the pre-alignment distributions.
  • Figure 4: Illustration of Theorem \ref{['thm_realizable']}.
  • Figure 5: Overview of the MAP procedure, which 1) lets a user specify the desirable levels of expected levels for all values of interest, also referred to as a Value Palette, 2) checks whether the specified palette admits a feasible solution of model alignment, 3) actually aligns the model by using a single MAP-guided reward function. More details will be provided in Section \ref{['sec_method']}.
  • ...and 5 more figures

Theorems & Definitions (8)

  • Theorem 1: Representation of MAP solution
  • Remark 1: Interpretation of $\bm \lambda$
  • Remark 2: Choice of Value Palette $\bm{c}$
  • Remark 3: Source of Reward Functions
  • Theorem 2: Solution of MAP
  • Theorem 3: Equivalent realizable value levels
  • Theorem 4
  • Theorem 5