R³A: Reinforced Reasoning for Relevance Assessment for RAG in User-Generated Content Platforms

Xiaowei Yuan, Lei Jin, Haoxin Zhang, Ziyang Huang, Yan Gao, YIWU, Yao Hu, Jun Zhao, Kang Liu

Keywords: Relevance Assessment, Reinforced Reasoning, Retrieval-Augmented Generation, User-Generated Content

TL;DR: This paper proposes the R³A method, a novel decomposed reasoning framework tailored for relevance assessment for RAG system in UGC scenarios.

Abstract: Retrieval-augmented generation (RAG) plays a critical role in user-generated content (UGC) platforms, but its effectiveness critically depends on accurate query–document relevance assessment. Despite recent advances in applying large language models (LLMs) to relevance modeling, UGC platforms present unique challenges: 1) ambiguous user intent due to sparse user feedback in RAG scenarios, and 2) asymmetric relevance, where relevance is driven by localized answer-bearing content rather than global query–document similarity. To address these issues, we propose the Reinforced Reasoning model for Relevance Assessment (R³A), which decomposes relevance assessment into intent inference and evidence grounding. R³A leverages auxiliary high-clicked documents to infer latent query intent, and extracts verbatim evidence fragments to ground relevance decisions, reducing noise sensitivity and improving asymmetric relevance modeling. Experimental results demonstrate that R³A substantially outperforms strong baselines on offline benchmarks, while the distilled R³A-1.5B model achieves significant gains in large-scale online A/B testing, effectively balancing performance and practical deployability.

Submission Type: Deployed

Submission Number: 23