Comprehensive Comparison of RAG Methods Across Multi-Domain Conversational QA

2026-02-10

AI Summary

This study addresses the gap in existing retrieval-augmented generation (RAG) research, which has predominantly focused on single-turn question answering and lacks systematic evaluation in multi-turn dialogue settings. Under a unified experimental framework, the authors present the first comprehensive comparison of multiple RAG approaches—including vanilla RAG, reranking, and hybrid strategies combining BM25 with HyDE—across eight multi-domain conversational QA datasets, jointly evaluating both retrieval and generation performance. The findings reveal that methodological complexity is not decisive; rather, the alignment between retrieval strategy and dataset structure is more critical. Simple yet robust methods such as reranking and hybrid BM25–HyDE consistently outperform vanilla RAG, while some advanced techniques even underperform a no-RAG baseline. Performance is also significantly influenced by dataset characteristics and dialogue length.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Natural Language Processing: Question Answering
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs

Abstract

Conversational question answering increasingly relies on retrieval-augmented generation (RAG) to ground large language models (LLMs) in external knowledge. Yet, most existing studies evaluate RAG methods in isolation and primarily focus on single-turn settings. This paper addresses the lack of a systematic comparison of RAG methods for multi-turn conversational QA, where dialogue history, coreference, and shifting user intent substantially complicate retrieval. We present a comprehensive empirical study of vanilla and advanced RAG methods across eight diverse conversational QA datasets spanning multiple domains. Using a unified experimental setup, we evaluate retrieval quality and answer generation using generator and retrieval metrics, and analyze how performance evolves across conversation turns. Our results show that robust yet straightforward methods, such as reranking, hybrid BM25, and HyDE, consistently outperform vanilla RAG. In contrast, several advanced techniques fail to yield gains and can even degrade performance below the No-RAG baseline. We further demonstrate that dataset characteristics and dialogue length strongly influence retrieval effectiveness, explaining why no single RAG strategy dominates across settings. Overall, our findings indicate that effective conversational RAG depends less on method complexity than on alignment between the retrieval strategy and the dataset structure. We publish the code used.\footnote{\href{https://github.com/Klejda-A/exp-rag.git}{GitHub Repository}}

Problem

• retrieval-augmented generation
• conversational question answering
• multi-turn dialogue
• systematic comparison
• external knowledge grounding

Innovation

• Retrieval-Augmented Generation
• Conversational QA
• Multi-turn Dialogue
• RAG Evaluation
• Dataset-Strategy Alignment