Typed- RAG : Type-Aware Decomposition of Non-Factoid Questions for Retrieval-Augmented Generation (2025)

Abstract: Non-factoid question answering (NFQA) poses a significant challenge due to its open-ended nature, diverse intents, and the necessity for multi-aspect reasoning, rendering conventional retrieval-augmented generation (RAG) approaches insufficient. To address this, we introduce Typed-RAG, a type-aware framework utilizing multi-aspect query decomposition tailored specifically for NFQA. Typed-RAG categorizes NFQs into distinct types—such as debate, experience, and comparison—and decomposes them into single-aspect sub-queries for targeted retrieval and generation. By synthesizing the retrieved results of these sub-queries, Typed-RAG generates more informative and contextually relevant responses. Additionally, we present Wiki-NFQA, a novel benchmark dataset encompassing diverse NFQ types. Experimental evaluation demonstrates that TypeRAG consistently outperforms baseline approaches, confirming the effectiveness of type-aware decomposition in improving both retrieval quality and answer generation for NFQA tasks.

## introduction traditional and current question answering (qa) systems @xcite @xcite @xcite have primarily addressed factoid questions-queries seeking specific, verifiable information that yield concise, objective answers like "when was google founded?". however, real-world information needs extend far beyond such simple factual queries. non-factoid questions (nfqs) represent a fundamentally different challenge from traditional factoid questions, as they require elaborate, interpretive responses that integrate multiple perspectives rather than retrieving single facts @xcite . these questions-encompassing comparative evaluations, personal experiences, and openended discussions-reflect the rich, multifaceted nature of human information seeking. figure 1 illustrates the key differences between factoid questions and nfqs, as well as their example answers. despite their prevalence in practical settings @xcite , current approaches to non-factoid question answering (nfqa) face significant limitations. the core challenge lies in the inherent complexity and heterogeneity of nfqs, which vary along multiple dimensions including question intent, aspect directionality, and degree of contrast between perspectives. existing methods fail to adequately address this diversity. type-specific approaches to nfqa @xcite -methods that focus exclusively on particular nfq types-struggle to generalize across the full spectrum of nfq types, whereas retrieval-augmented generation (rag) systems @xcite , de-spite improving contextuality, produce overly homogeneous responses that lack the multi-faceted depth essential for comprehensive answers. ultimately, the fundamental challenge is adapting retrieval and generation strategies to the specific characteristics of each nfq type. to address these limitations, we propose typed-rag, a type-aware decomposition approach that fundamentally reimagines nfqa within the rag paradigm. our approach integrates question type classification directly into the rag paradigm, enabling tailored retrieval and generation strategies for distinct nfq types. the key innovation lies in decomposing multi-aspect nfqs into single-aspect sub-queries, allowing targeted retrieval for each aspect before synthesizing a comprehensive response. this decomposition approach ensures that generated answers align with user intent while capturing the full complexity of the question. in order to assess the performance of typed-rag, we evaluate it on wiki-nfqa, a new benchmark dataset derived from wikipedia that encompasses a broad spectrum of nfq types. our results demonstrate that typed-rag significantly outperforms baseline systems, including standard approaches using llms and rag, in handling nfq complexity and delivering nuanced, intent-aligned answers. our main contributions are as follows: by addressing the unique challenges of nfqa through type-aware decomposition, our work bridges the gap between complex user information needs and current qa system capabilities, advancing the development of more adaptive and contextsensitive qa technologies. ## method in this section, we introduce typed-rag, a novel rag pipeline designed specifically for non-factoid question (nfq) types. figure 2 visually illustrates the overall processing mechanisms of the multiaspect decomposer and the answer aggregator for each nfq type. nfqs are classified into one of six types using a pre-trained type classifier @xcite . subsequently, queries are preprocessed through the multi-aspect decomposer, where each type's characteristics and the underlying intent of the questions are considered. the multi-aspect decomposer primarily consists of two modules: the single-aspect query generator and the keyword extractor. these modules operate selectively based on the question type and perspective, leveraging few-shot learning and prompt engineering techniques to effectively transform queries according to their respective categories. the decomposed queries are then processed by one or multiple retrievers to retrieve highly relevant passages. optionally, the retrieved passages may be re-ranked using a reranker to enhance the quality of results. based on the retrieved information, the generator produces the final answers. if multiple candidate answers are available, the answer aggregator integrates these candidates to form a unified response. this process is structured based on prompt engineering. detailed prompt configurations for each module are presented in appendix a.4. as previously discussed, nfqs inherently involve multiple perspectives, making them challenging to handle effectively using conventional rag approaches. considering these distinct characteristics, we propose a type-aware pipeline specifically tailored to nfqs, capable of generating responses ## experimental results we evaluate typed-rag on the wiki-nfqa dataset across all nfq categories and model configurations. as shown in ## conclusion in this paper, we introduced typed-rag, a novel rag-based framework for non-factoid question answering (nfqa) that incorporates type-aware multi-aspect decomposition. by first classifying each nfq into a specific category and then decomposing it into focused sub-queries, typed-rag enables targeted retrieval and answer generation for each aspect. the retrieved sub-responses are aggregated to produce comprehensive, nuanced answers that better address the diverse requirements of non-factoid questions. to support evaluation, we also curated wiki-nfqa, a benchmark dataset covering a wide range of nfq types. experimental results on wiki-nfqa dataset show that typed-rag consistently outperforms both llm-only and standard rag baselines across all question types and scorer llm settings. these findings validate the effectiveness and robustness of type-aware multi-aspect decomposition in enhancing both retrieval quality and answer relevance for nfqa. future work could explore extending typed-rag to incorporate more fine-grained question types and further refine the decomposition strategies. additionally, applying our approach to other specific domains or applying fine-tuning methods and integrating it with more sophisticated retrieval mechanisms could further improve the performance and adaptability of nfqa systems.