Counterfactual Reasoning for Steerable Pluralistic Value Alignment of Large Language Models

2025-10-21

AI Summary

Current large language models (LLMs) predominantly rely on averaged alignment criteria (e.g., HHH), rendering them ill-suited for complex, imbalanced value systems across diverse cultural contexts. We identify two key bottlenecks: (1) high-order interdependencies and relative prioritization among values—termed *value complexity*—and (2) insufficient controllability over marginalized groups’ values—termed *value manipulability*. To address these, we propose COUPLE, the first framework to integrate structural causal models (SCMs) into value alignment. COUPLE explicitly encodes the causal dependency structure among multidimensional values and models their counterfactual influence on model generations, enabling fine-grained, interpretable, and prioritized value steering. Experiments on a dual-value-system benchmark demonstrate that COUPLE significantly improves both alignment accuracy and controllability across heterogeneous value objectives, outperforming state-of-the-art baselines.

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Large Vision Models

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

As large language models (LLMs) become increasingly integrated into applications serving users across diverse cultures, communities and demographics, it is critical to align LLMs with pluralistic human values beyond average principles (e.g., HHH). In psychological and social value theories such as Schwartz's Value Theory, pluralistic values are represented by multiple value dimensions paired with various priorities. However, existing methods encounter two challenges when aligning with such fine-grained value objectives: 1) they often treat multiple values as independent and equally important, ignoring their interdependence and relative priorities (value complexity); 2) they struggle to precisely control nuanced value priorities, especially those underrepresented ones (value steerability). To handle these challenges, we propose COUPLE, a COUnterfactual reasoning framework for PLuralistic valuE alignment. It introduces a structural causal model (SCM) to feature complex interdependency and prioritization among features, as well as the causal relationship between high-level value dimensions and behaviors. Moreover, it applies counterfactual reasoning to generate outputs aligned with any desired value objectives. Benefitting from explicit causal modeling, COUPLE also provides better interpretability. We evaluate COUPLE on two datasets with different value systems and demonstrate that COUPLE advances other baselines across diverse types of value objectives.

Problem

• Aligning LLMs with diverse cultural values beyond average principles
• Addressing value complexity and interdependence in pluralistic alignment
• Enabling precise control over nuanced value priorities and steerability

Innovation

• Structural causal model represents value interdependency and prioritization
• Counterfactual reasoning generates outputs for desired value objectives
• Framework enables steerable pluralistic alignment with interpretability