The Impact of Query Decomposition and Cross-Encoder Reranking in Multi-Hop Retrieval-Augmented Generation

Retrieval-Augmented Generation (RAG) has emerged as a promising paradigm for open-domain question answering. However, standard single-hop retrieval often fails on complex, multi-hop queries where the answer requires synthesizing information from disparate documents. In this work, we propose an enhanced Multi-Hop RAG pipeline augmented with Cross-Encoder Reranking to address the challenges of reasoning across multiple documents.

Our approach decomposes complex queries into self-contained sub-questions and employs a Cross-Encoder to rerank candidates at each retrieval step, mitigating the "semantic drift" inherent in dense vector search. We systematically evaluate our system against two baselines—Standard Single-Hop RAG and Decomposed Multi-Hop RAG—using a curated subset of the HotpotQA dataset. Experimental results demonstrate that our proposed method achieves superior accuracy (62%, a 20% gain over the single-hop baseline) by effectively filtering distractors.

Furthermore, our ablation studies reveal a fundamental "Recall Ceiling" in dense retrieval, where blindly increasing the candidate pool yields diminishing returns. Based on these findings, we identify a "Wide Net, Tight Filter" strategy as the Pareto-optimal configuration for balancing reasoning accuracy with system latency.