Quantifying Retriever-Generator Alignment in RAG with Local Explanations

2026-01-29 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, quick

Summary

The paper "Quantifying Retriever-Generator Alignment in RAG with Local Explanations" introduces RAG-E, an end-to-end explainability framework designed to quantify the interaction between dense retrievers and language models in Retrieval-Augmented Generation (RAG) systems. RAG-E employs mathematically grounded attribution methods, adapting Integrated Gradients for retriever analysis and proposing a Monte Carlo-stabilized Shapley Value approximation for generator attribution. A key component is the Weighted Attribution-Relevance Gap (WARG) metric, which measures how closely the generator's document usage aligns with retriever rankings. Experiments conducted on PopQA, QAMPARI, and TREC CAST datasets revealed significant misalignment, showing that generators frequently disregard top-ranked documents in favor of less relevant ones. The research demonstrates that WARG effectively captures retriever-generator alignment, outperforming Pearson and Spearman correlations, and serves as a reliable indicator of RAG system performance.

Key takeaway

For MLOps Engineers deploying RAG systems in high-stakes domains, you must actively audit the retriever-generator interaction. This research reveals significant misalignment, where generators often ignore top-ranked documents. Implement frameworks like RAG-E and metrics such as WARG to quantify this alignment. Understanding your system's WARG score will help you diagnose performance issues and ensure your RAG outputs are truly grounded in relevant external documents, improving reliability and transparency.

Key insights

RAG-E quantifies retriever-generator misalignment using attribution methods and the WARG metric, revealing generators often ignore top-ranked documents.

Principles

• RAG systems exhibit substantial retriever-generator misalignment.
• Generator reliance on top-ranked documents is often low.
• WARG metric indicates RAG performance and alignment.

Method

RAG-E adapts Integrated Gradients for retriever analysis and uses Monte Carlo-stabilized Shapley Value approximation for generator attribution, then calculates WARG.

In practice

• Audit RAG systems for retriever-generator alignment.
• Use WARG to identify RAG performance issues.
• Evaluate generator reliance on retrieved documents.

Topics

• Retrieval-Augmented Generation
• RAG Explainability
• Retriever-Generator Alignment
• Attribution Methods
• WARG Metric
• Large Language Models

Best for: Research Scientist, AI Architect, AI Engineer, AI Scientist, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.