Adaptive Pluralistic Alignment: A pipeline for dynamic artificial democracy
- URL: http://arxiv.org/abs/2605.01642v1
- Date: Sat, 02 May 2026 23:22:23 GMT
- Title: Adaptive Pluralistic Alignment: A pipeline for dynamic artificial democracy
- Abstract summary: We introduce Adaptive Pluralistic Alignment (APA), a modular pipeline for updating pluralistically aligned AI systems to track evolving values.<n>APA has three stages: learning compact personalized reward models via low-rank reward basis decomposition, (2) using these models as a jury that collectively selects among candidate outputs through social-choice-theoretic voting, and (3) efficiently adapting the jury over time by fitting new annotator weights over the fixed reward bases as values shift.
- Score: 2.653040099621359
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Prevailing alignment methods target a fixed set of preferences and therefore risk forcing value lock-in as societal norms evolve over time. We introduce Adaptive Pluralistic Alignment (APA), a modular pipeline for updating pluralistically aligned AI systems to track evolving values and avoid value lock-in without repeating costly pretraining or large-scale data collection. APA has three stages: (1) learning compact personalized reward models via low-rank reward basis decomposition, (2) using these models as a jury that collectively selects among candidate outputs through social-choice-theoretic voting, and (3) efficiently adapting the jury over time by fitting new annotator weights over the fixed reward bases as values shift. The resulting system is efficient, explainable, steerable, and modular. We implement a proof-of-concept instantiation using the PRISM multi-user alignment dataset and simulated historical annotators, and provide preliminary analysis showing that jury composition and the choice of voting rule can substantially affect outcomes, particularly when jury preferences are heterogeneous. We provide full code and resulting preference datasets at https://anonymous.4open.science/r/apa.
Related papers
- REAR: Test-time Preference Realignment through Reward Decomposition [68.09214603569744]
We introduce a novel framework that models the task as a realignment problem, since the base model often fails to align with the stated preference.<n>We derive a REAlignment Reward (REAR) that selectively rescales the proportions of these two reward terms.<n>We show that REAR can be formulated as a linear combination of token-level policy log-probabilities, making it computationally efficient and easy to integrate with various TTS algorithms.
arXiv Detail & Related papers (2026-06-29T14:17:53Z) - Response Time Enhances Alignment with Heterogeneous Preferences [49.69696266152175]
We show that augmenting preference datasets with a simple, secondary signal can restore identifiability of the population's average preference.<n>Our results bring promises and open up new opportunities for future data-collection pipelines.
arXiv Detail & Related papers (2026-05-07T22:05:23Z) - Dependence-Aware Label Aggregation for LLM-as-a-Judge via Ising Models [55.94503936470247]
Large-scale AI evaluation increasingly relies on aggregating binary judgments from $K$ annotators, including judges.<n>Most classical methods assume annotators are conditionally independent given the true label $Yin0,1$, an assumption often violated by LLM judges.<n>We study label aggregation through a hierarchy of dependence-aware models based on Ising graphical models and latent factors.
arXiv Detail & Related papers (2026-01-29T21:26:50Z) - Direct Preference Optimization with Unobserved Preference Heterogeneity: The Necessity of Ternary Preferences [14.686788596611246]
Reinforcement Learning from Human Feedback (RLHF) has become central to aligning large language models with human values.<n>Recent alternatives such as Direct Preference Optimization (DPO) simplify this pipeline by directly optimizing on preferences.<n>We propose a theoretical and algorithmic framework for fairness and personalization for diverse users in generative model alignment.
arXiv Detail & Related papers (2025-10-17T15:00:40Z) - Steerable Adversarial Scenario Generation through Test-Time Preference Alignment [58.37104890690234]
Adversarial scenario generation is a cost-effective approach for safety assessment of autonomous driving systems.<n>We introduce a new framework named textbfSteerable textbfAdversarial scenario textbfGEnerator (SAGE)<n>SAGE enables fine-grained test-time control over the trade-off between adversariality and realism without any retraining.
arXiv Detail & Related papers (2025-09-24T13:27:35Z) - Alternates, Assemble! Selecting Optimal Alternates for Citizens' Assemblies [1.5624421399300306]
Citizens' assemblies are an influential form of deliberative democracy, where randomly selected people discuss policy questions.<n> dropouts are replaced by preselected alternates, but existing methods do not address how to choose these alternates.<n>We introduce an optimization framework for alternate selection.
arXiv Detail & Related papers (2025-06-02T17:48:33Z) - Few-shot Steerable Alignment: Adapting Rewards and LLM Policies with Neural Processes [50.544186914115045]
Large language models (LLMs) are increasingly embedded in everyday applications.<n> Ensuring their alignment with the diverse preferences of individual users has become a critical challenge.<n>We present a novel framework for few-shot steerable alignment.
arXiv Detail & Related papers (2024-12-18T16:14:59Z) - Beyond the Binary: Capturing Diverse Preferences With Reward Regularization [15.518838657050173]
We argue that this reliance on binary choices does not capture the broader, aggregate preferences of the target user in real-world tasks.<n>We introduce a simple yet effective method that augments existing binary preference datasets with synthetic preference judgments to estimate potential user disagreement.
arXiv Detail & Related papers (2024-12-05T02:35:46Z) - Direct Preference Optimization With Unobserved Preference Heterogeneity: The Necessity of Ternary Preferences [14.686788596611246]
Reinforcement Learning from Human Feedback (RLHF) has become central to aligning large language models with human values.<n>Recent alternatives such as Direct Preference Optimization (DPO) simplify this pipeline by directly optimizing on preferences.<n>We propose a theoretical and algorithmic framework for fairness and personalization for diverse users in generative model alignment.
arXiv Detail & Related papers (2024-05-23T21:25:20Z) - Fine-Tuning Language Models with Reward Learning on Policy [68.70065254564642]
Reinforcement learning from human feedback (RLHF) has emerged as an effective approach to aligning large language models (LLMs) to human preferences.
Despite its popularity, (fixed) reward models may suffer from inaccurate off-distribution.
We propose reward learning on policy (RLP), an unsupervised framework that refines a reward model using policy samples to keep it on-distribution.
arXiv Detail & Related papers (2024-03-28T10:02:10Z) - Efficient Weighting Schemes for Auditing Instant-Runoff Voting Elections [57.67176250198289]
AWAIRE involves adaptively weighted averages of test statistics, essentially "learning" an effective set of hypotheses to test.
We explore schemes and settings more extensively, to identify and recommend efficient choices for practice.
A limitation of the current AWAIRE implementation is its restriction to a small number of candidates.
arXiv Detail & Related papers (2024-02-18T10:13:01Z) - Few-Shot Named Entity Recognition: A Comprehensive Study [92.40991050806544]
We investigate three schemes to improve the model generalization ability for few-shot settings.
We perform empirical comparisons on 10 public NER datasets with various proportions of labeled data.
We create new state-of-the-art results on both few-shot and training-free settings.
arXiv Detail & Related papers (2020-12-29T23:43:16Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.