Towards Faithful Industrial RAG: A Reinforced Co-adaptation Framework for Advertising QA
arXiv:2602.22584v1 Announce Type: new Abstract: Industrial advertising question answering (QA) is a high-stakes task in which hallucinated content, particularly fabricated URLs, can lead to financial loss, compliance violations, and legal risk. Although Retrieval-Augmented Generation (RAG) is widely adopted, deploying it in production remains challenging because industrial knowledge is inherently relational, frequently updated, and insufficiently aligned with generation objectives. We propose a reinforced co-adaptation framework that jointly optimizes retrieval and generation through two components: (1) Graph-aware Retrieval (GraphRAG), which models entity-relation structure over a high-citation knowledge subgraph for multi-hop, domain-specific evidence selection; and (2) evidence-constrained reinforcement learning via Group Relative Policy Optimization (GRPO) with multi-dimensional rewards covering faithfulness, style compliance, safety, and URL validity. Experiments on an internal a
arXiv:2602.22584v1 Announce Type: new Abstract: Industrial advertising question answering (QA) is a high-stakes task in which hallucinated content, particularly fabricated URLs, can lead to financial loss, compliance violations, and legal risk. Although Retrieval-Augmented Generation (RAG) is widely adopted, deploying it in production remains challenging because industrial knowledge is inherently relational, frequently updated, and insufficiently aligned with generation objectives. We propose a reinforced co-adaptation framework that jointly optimizes retrieval and generation through two components: (1) Graph-aware Retrieval (GraphRAG), which models entity-relation structure over a high-citation knowledge subgraph for multi-hop, domain-specific evidence selection; and (2) evidence-constrained reinforcement learning via Group Relative Policy Optimization (GRPO) with multi-dimensional rewards covering faithfulness, style compliance, safety, and URL validity. Experiments on an internal advertising QA dataset show consistent gains across expert-judged dimensions including accuracy, completeness, and safety, while reducing the hallucination rate by 72\%. A two-week online A/B test demonstrates a 28.6\% increase in like rate, a 46.2\% decrease in dislike rate, and a 92.7\% reduction in URL hallucination. The system has been running in production for over half a year and has served millions of QA interactions.
Executive Summary
The article 'Towards Faithful Industrial RAG: A Reinforced Co-adaptation Framework for Advertising QA' introduces a novel framework aimed at enhancing the reliability and accuracy of Retrieval-Augmented Generation (RAG) in industrial advertising question-answering systems. The authors address the critical issue of hallucinated content, particularly fabricated URLs, which can lead to financial loss, compliance violations, and legal risks. The proposed framework consists of two main components: Graph-aware Retrieval (GraphRAG) and evidence-constrained reinforcement learning via Group Relative Policy Optimization (GRPO). The experiments conducted on an internal advertising QA dataset demonstrate significant improvements in accuracy, completeness, safety, and a substantial reduction in hallucination rates. The system has been successfully deployed in production, serving millions of QA interactions and showing positive outcomes in user engagement metrics.
Key Points
- ▸ Introduction of a reinforced co-adaptation framework for industrial advertising QA.
- ▸ Graph-aware Retrieval (GraphRAG) models entity-relation structure for domain-specific evidence selection.
- ▸ Evidence-constrained reinforcement learning via GRPO optimizes multiple dimensions including faithfulness, style compliance, safety, and URL validity.
- ▸ Experiments show significant improvements in accuracy, completeness, safety, and a 72% reduction in hallucination rate.
- ▸ Online A/B test results include a 28.6% increase in like rate, a 46.2% decrease in dislike rate, and a 92.7% reduction in URL hallucination.
Merits
Innovative Framework
The proposed framework addresses a critical gap in the current RAG systems by introducing a co-adaptation approach that jointly optimizes retrieval and generation.
Comprehensive Evaluation
The article provides a thorough evaluation through both offline experiments and online A/B testing, demonstrating the practical effectiveness of the proposed framework.
Real-World Impact
The system has been successfully deployed in production, serving millions of interactions and showing measurable improvements in user engagement and content quality.
Demerits
Limited Dataset
The experiments are conducted on an internal advertising QA dataset, which may limit the generalizability of the findings to other domains or industries.
Complexity
The framework introduces additional complexity in terms of implementation and computational resources, which may be a barrier for smaller organizations.
Dependence on High-Citation Knowledge Subgraph
The effectiveness of GraphRAG relies on the availability and quality of a high-citation knowledge subgraph, which may not be readily available in all contexts.
Expert Commentary
The article presents a significant advancement in the field of AI-powered question-answering systems, particularly in high-stakes industrial advertising. The reinforced co-adaptation framework proposed by the authors addresses a critical gap in current RAG systems by jointly optimizing retrieval and generation processes. The introduction of GraphRAG and GRPO represents a novel approach to enhancing the faithfulness and reliability of AI-generated content. The comprehensive evaluation, including both offline experiments and online A/B testing, provides strong evidence of the framework's effectiveness. The successful deployment in production, serving millions of interactions, further underscores the practical impact of this research. However, the reliance on a high-citation knowledge subgraph and the complexity of the framework may pose challenges for broader adoption. Overall, this article makes a valuable contribution to the ongoing efforts to improve the reliability and safety of AI systems in high-stakes environments.
Recommendations
- ✓ Further research should explore the generalizability of the proposed framework to other domains and industries beyond advertising QA.
- ✓ Future work should investigate the scalability and computational efficiency of the framework to make it more accessible to smaller organizations.
- ✓ Policymakers and industry stakeholders should collaborate to develop robust evaluation metrics and regulatory frameworks to ensure the reliability and safety of AI systems in production environments.
