aiqbench analysis
This research addresses the limitation of current Large Language Models in handling complex instructions by introducing Cross-Relational Preference Learning (CRPL). The framework explicitly models relationships between different instruction constraints to generate more diverse preference data, leading to measurable improvements across various backbones and benchmarks. By rigorously assessing response satisfaction through an atomic constraint-based mechanism, this work advances the state-of-the-art in instruction-following capabilities without relying on quantum or incidental AI features.
Cross-Relational Preference Learning for Better LLM Instruction Following
arXiv:2608.29352v1 Announce Type: new Abstract: Large Language Models (LLMs) still exhibit limited capability in following complex instructions. While existing approaches often rely on preference learning to enhance this ability, they typically overlook the relationships between the permissible response spaces of different instructions, which restricts a model to align with subtle and diverse constraint variations. To address this, we propose Cross-Relational Preference Learning (CRPL), a novel framework for constructing preference data that explicitly models inter-instruction relationships through two key techniques: Cross-Relationship Perturbation and Cross-Region Pair Sampling. This enables the generation of more diverse preference data that captures a wide spectrum of constraint variations. Additionally, we introduce an atomic constraint-based verification mechanism to rigorously assess response satisfaction, ensuring high-quality preference pair construction. Extensive experiments across multiple preference learning methods (e.g., DPO, KTO), LLM backbones and four instruction-following benchmarks demonstrate that our approach achieves substantial improvements over prior baselines and exhibits strong generalization.
Abstract:Large Language Models (LLMs) still exhibit limited capability in following complex instructions. While existing approaches often rely on preference learning to enhance this ability, they typically overlook the relationships between the permissible response spaces of different instructions, which restricts a model to align with subtle and diverse constraint variations. To address this, we propose Cross-Relational Preference Learning (CRPL), a novel framework for constructing preference data that explicitly models inter-instruction relationships through two key techniques: Cross-Relationship Perturbation and Cross-Region Pair Sampling. This enables the generation of more diverse preference data that captures a wide spectrum of constraint variations. Additionally, we introduce an atomic constraint-based verification mechanism to rigorously assess response satisfaction, ensuring high-quality preference pair construction. Extensive experiments across multiple preference learning methods (e.g., DPO, KTO), LLM backbones and four instruction-following benchmarks demonstrate that our approach achieves substantial improvements over prior baselines and exhibits strong generalization.
Artificial Intelligence (cs.AI)
arXiv:2608.29352 [cs.AI]
(or arXiv:2608.29352v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2608.29352 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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