TaSR-RAG: Taxonomy-guided Structured Reasoning for Retrieval-Augmented Generation

TL;DR

Experiments on multiple multi-hop question answering benchmarks show that TaSR-RAG consistently outperforms strong RAG and structured-RAG baselines by up to 14%, while producing clearer evidence attribution and more faithful reasoning traces.

Abstract

Retrieval-Augmented Generation (RAG) helps large language models (LLMs) answer knowledge-intensive and time-sensitive questions by conditioning generation on external evidence. However, most RAG systems still retrieve unstructured chunks and rely on one-shot generation, which often yields redundant context, low information density, and brittle multi-hop reasoning. While structured RAG pipelines can improve grounding, they typically require costly and error-prone graph construction or impose rigid entity-centric structures that do not align with the query's reasoning chain. We propose \textsc{TaSR-RAG}, a taxonomy-guided structured reasoning framework for evidence selection. We represent both queries and documents as relational triples, and constrain entity semantics with a lightweight two-level taxonomy to balance generalization and precision. Given a complex question, \textsc{TaSR-RAG} decomposes it into an ordered sequence of triple sub-queries with explicit latent variables, then performs step-wise evidence selection via hybrid triple matching that combines semantic similarity over raw triples with structural consistency over typed triples. By maintaining an explicit entity binding table across steps, \textsc{TaSR-RAG} resolves intermediate variables and reduces entity conflation without explicit graph construction or exhaustive search. Experiments on multiple multi-hop question answering benchmarks show that \textsc{TaSR-RAG} consistently outperforms strong RAG and structured-RAG baselines by up to 14\%, while producing clearer evidence attribution and more faithful reasoning traces.

Figures (5)

• Figure 1: Overview of TaSR-RAG. We illustrate Document-level triple extraction and taxonomy-guided entity typing, query decomposition with latent variables, hybrid triple matching for document reranking, and sequential entity binding.
• Figure 2: Error distribution in our manual analysis. The left figure illustrates Matching Errors and right figure illustrates Generation Errors.
• Figure 3: Performance--efficiency trade-off under offline preprocessing and inference-time execution. TaSR-RAG (ours) achieves the best overall balance, avoiding the high offline cost of graph-based methods while maintaining low inference-time overhead.
• Figure 4: Performance by reasoning depth (hop count). We report results for hop counts with non-empty support. While RAG performance degrades as reasoning depth increases, TaSR-RAG maintains strong performance and exhibits increasingly larger gains in deeper reasoning regimes, with particularly pronounced improvements at 3-hop and beyond.
• Figure 5: Performance comparison between TaSR-RAG and standard RAG across different retrieval budgets (Top-K) on three multi-hop QA datasets. TaSR-RAG consistently outperforms RAG across all settings, with particularly strong gains on 2WikimQA (over 100% relative improvement at Top-10) and substantial improvements on HotpotQA and Bamboogle.