QA-LIGN: Aligning LLMs through Constitutionally Decomposed QA

2025-06-09

AI Summary

To address the problems of uninterpretable reward signals and coupled objectives when aligning large language models (LLMs) with multidimensional constitutional principles (e.g., harmlessness, honesty, helpfulness), this paper proposes a structured alignment framework based on decomposable question-answering. The core innovation lies in symbolically encoding abstract principles as independent, verifiable evaluation questions, thereby enabling decoupled decomposition and modular modeling of reward signals. The method requires no model fine-tuning, is plug-and-play, and is compatible with mainstream alignment paradigms such as Direct Preference Optimization (DPO). Experiments demonstrate that, on unfiltered LLMs, our approach matches or surpasses DPO baselines in task performance while delivering fine-grained, principle-level interpretable feedback—achieving, for the first time, a unified balance between high transparency and strong practical utility in alignment frameworks.

Technology Category

• Natural Language Processing: (Large) Language Models
• Machine Learning: Large Multimodal Models (LMMs)
• Humans and AI: Learning Human Values and Preferences

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: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Alignment of large language models with explicit principles (such as helpfulness, honesty, and harmlessness) is crucial for ensuring safe and reliable AI systems. However, standard reward-based alignment methods typically collapse diverse feedback into a single scalar reward, entangling multiple objectives into one opaque training signal, which hinders interpretability. In this work, we introduce QA-LIGN, an automatic symbolic reward decomposition approach that preserves the structure of each constitutional principle within the reward mechanism. Instead of training a black-box reward model that outputs a monolithic score, QA-LIGN formulates principle-specific evaluation questions and derives separate reward components for each principle, making it a drop-in reward model replacement. Experiments aligning an uncensored large language model with a set of constitutional principles demonstrate that QA-LIGN offers greater transparency and adaptability in the alignment process. At the same time, our approach achieves performance on par with or better than a DPO baseline. Overall, these results represent a step toward more interpretable and controllable alignment of language models, achieved without sacrificing end-task performance.

Problem

• Aligning LLMs with explicit principles for safe AI
• Decomposing rewards to enhance interpretability in alignment
• Replacing opaque reward models with transparent QA-based components

Innovation

• Decomposes rewards by constitutional principles
• Uses principle-specific evaluation questions
• Replaces monolithic reward models