From Preferences to Principles: Rubric-Based Alignment for Grounded Knowledge Answers

Abstract:Designing effective reward signals for open-domain question answering is challenging because high-quality responses must simultaneously satisfy multiple aspects of answer quality that are difficult to capture with a holistic scalar objective. We introduce a rubric-based reward framework that generates query-specific rubrics grounded in retrieved evidence and decomposed into multiple quality dimensions, providing fine-grained supervision during post-training. Averaged across three evaluation axes (composition, grounding, and instruction-following), our approach improves over the instruction-tuned baseline by 6.5% and over flat rubric variants by 4%, with consistent gains across all evaluation datasets. Conditioning rubrics on retrieved evidence improves factual support, while decomposing rubrics into quality-specific dimensions further improves coherence, organization, and adherence to query requirements. Our results show that grounded, multi-dimensional rubrics provide more effective reward supervision for complex open-domain question answering.

Computation and Language (cs.CL)

arXiv:2608.23812 [cs.CL]

(or arXiv:2608.23812v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.23812 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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