Robust and explainable Retrieval-Augmented Generation under retrieval noise
TL;DR
Testing how retrieval noise affects RAG and whether reranking, citation-aware generation, and lightweight verification can improve system behaviour suggests that robust and explainable RAG is a multi-objective problem.
Abstract
Retrieval-Augmented Generation (RAG) improves language-model answers by retrieving external evidence before generation. However, its reliability depends on the retrieved context. In real settings, passages can be irrelevant, incomplete, conflicting, or poorly ordered. These problems may reduce accuracy and explainability. This study tests how retrieval noise affects RAG and whether reranking, citation-aware generation, and lightweight verification can improve system behaviour. A controlled experiment was conducted on a small HotpotQA subset using BM25, Sentence-BERT, and FAISS. Four systems were compared: vanilla RAG, reranking-only, citation-only, and a full enhanced system. Results show that reranking achieved the highest average noisy F1, but the gain over vanilla RAG was small. The full enhanced system achieved better faithfulness, groundedness, and citation precision, but did not improve average noisy F1. This suggests that robust and explainable RAG is a multi-objective problem.