arXiv:2601.21803v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) systems combine dense retrievers and language models to ground their outputs in external documents. However, the interaction between these components remains opaque, creating challenges for deployment in high-stakes domains. We present RAG-E, an end-to-end explainability framework that quantifies retriever-generator alignment through mathematically grounded attribution methods. Our approach adapts Integrated Gradients for retriever analysis, proposes a Monte Carlo-stabilized Shapley Value approximation for
The increasing deployment of RAG systems in critical applications necessitates greater transparency and trustworthiness, driving research into explainability frameworks.
This research provides a framework for understanding and debugging complex AI systems, crucial for their adoption in high-stakes environments and for ensuring responsible AI development.
The ability to quantify retriever-generator alignment allows for more reliable RAG deployments, moving RAG from experimental to production-ready for sensitive tasks.
- · AI developers
- · High-stakes industries (e.g., finance, healthcare)
- · AI explainability researchers
- · Users of RAG-based AI systems
- · Opaque AI systems
- · Organizations relying solely on black-box AI
- · AI developers ignoring explainability
Improved debugging and performance optimization of RAG systems.
Increased trust and adoption of RAG in enterprise and regulated sectors.
Potential for new regulatory standards for RAG explainability and alignment.
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Read at arXiv cs.CL