Hi-Q: Hierarchical Evidence-guided Query Refinement for Multi-Hop Question Answering

• URL: http://arxiv.org/abs/2608.30468v1
• Date: Mon, 31 Aug 2026 08:55:07 GMT
• Title: Hi-Q: Hierarchical Evidence-guided Query Refinement for Multi-Hop Question Answering
• Abstract summary: Hi-Q is an evidence-conditioned framework for hierarchical query refinement.<n>We evaluate Hi-Q on three multi-hop Question Answering (QA) benchmarks.
• Score: 13.249024309069236
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A central bottleneck in multi-hop Question Answering (QA) is that the granularity at which a question is expressed often differs from the granularity at which corpus evidence is retrievable. Existing methods address this mismatch by imposing fixed graph structures over the corpus, by iteratively reformulating the query, or by executing a generated program over it, but these strategies do not explicitly decide when a query unit is already supported by evidence and when it should be refined. We formulate this bottleneck as retrievable granularity discovery and introduce Hi-Q, an evidence-conditioned framework for hierarchical query refinement. At each query node, a resolution operator tests whether retrieved evidence supports the current query unit; resolved nodes terminate, while unresolved nodes are expanded by a dependency-preserving binary operator and checked by a semantic coverage verifier. Hi-Q therefore grows a query tree whose topology is determined by corpus support signals rather than by a fixed decomposition template or a pre-built graph. We evaluate Hi-Q on three multi-hop QA benchmarks, primarily under full-corpus retrieval, where dependent evidence must be located among open-domain distractors rather than within a small annotated pool. In this setting Hi-Q reaches 52.3 EM and 64.0 F1 averaged over the three benchmarks, ahead of the iterative retrieval baseline IRCoT by 15.1 EM / 18.2 F1 on that same average, and ahead of the graph-based RAG baseline PropRAG by 11.5 EM / 12.0 F1 on MuSiQue-full, without corpus-wide graph construction. In the restricted supporting/distractor setting used by prior work, Hi-Q likewise attains the best accuracy, with 57.9 EM and 69.3 F1 on average, ahead of PropRAG by 5.6 EM / 3.9 F1 and IRCoT by 13.7 EM / 15.8 F1. The project page is available at https://hi-q-project.github.io/.

Related papers

• PAGE-RAG: Provenance-Aware Graph Evidence Promotion for Fixed-Budget Multi-hop Retrieval-Augmented Generation [13.8041276969708]
  Multi-hop question answering in retrieval-augmented gener?ation (RAG) often benefits from retrieving beyond the few candidates that will finally be read.<n>We propose PAGE-RAG, a Provenance-Aware Graph Evidence promotion method that scores candidate paths with relevance.<n>As a plug-in, PAGE-RAG further improves all reported RAG backends, including reasoning-oriented, compression-based, graph-based, and document/chunk-level systems.
  arXiv  Detail & Related papers  (2026-08-30T12:33:58Z)
• Query-Aware Spreading Activation for Multi-Hop Retrieval over Knowledge Graphs [0.0]
  In most existing systems, the question only sets the seed nodes.<n>This architecture requires loading the full graph into Python memory and an iterative solver with a variable number of iterations.<n>We propose a spreading-activation method that achieves the same query-aware.<n>retrieval with a single per-step semantic gate.
  arXiv  Detail & Related papers  (2026-06-29T11:10:19Z)
• Ontology-Guided Evidence Path Inference for Multi-hop Knowledge Graph Question Answering [5.690260503175008]
  Knowledge graph question answering (KGQA) aims to answer natural-language questions by reasoning over structured facts.<n>Existing multi-hop KGQA methods mainly rely on topic-centered expansion, which faces two key challenges.<n>We propose OPI, an ontology-guided evidence path inference framework for multi-hop KGQA.
  arXiv  Detail & Related papers  (2026-06-26T13:40:51Z)
• QUBRIC: Co-Designing Queries and Rubrics for RL Beyond Verifiable Rewards [68.15832368034815]
  We present a framework that co-designs queries and rubrics.<n>QUBRIC achieves a +5.5 point gain on ArenaHard over the SFT baseline.
  arXiv  Detail & Related papers  (2026-06-02T17:53:04Z)
• MDER-DR: Multi-Hop Question Answering with Entity-Centric Summaries [2.756584457554517]
  Map-Disambiguate-Enrich-Reduce (MDER) generates context-derived triple descriptions and integrates them with entity-level summaries.<n>Decompose-Resolve (DR) decomposes user queries into resolvable triples and grounds them in the Knowledge Graph.<n>MDER and DR form an LLM-driven QA pipeline that is robust to sparse, incomplete, and complex relational data.
  arXiv  Detail & Related papers  (2026-03-11T18:38:44Z)
• Hierarchical Sequence Iteration for Heterogeneous Question Answering [27.22775290181187]
  This paper introduces HSEQ, a unified framework that linearizes documents, tables, and knowledge graphs into a reversible hierarchical sequence.<n> Experiments on HotpotQA (text), HybridQA/TAT-QA (table+text), and MetaQA (KG) show consistent EM/F1 gains over strong single-pass, multi-hop, and agentic RAG baselines with high efficiency.
  arXiv  Detail & Related papers  (2025-10-23T12:48:18Z)
• IfQA: A Dataset for Open-domain Question Answering under Counterfactual Presuppositions [54.23087908182134]
  We introduce the first large-scale counterfactual open-domain question-answering (QA) benchmarks, named IfQA. The IfQA dataset contains over 3,800 questions that were annotated by crowdworkers on relevant Wikipedia passages. The unique challenges posed by the IfQA benchmark will push open-domain QA research on both retrieval and counterfactual reasoning fronts.
  arXiv  Detail & Related papers  (2023-05-23T12:43:19Z)
• UniKGQA: Unified Retrieval and Reasoning for Solving Multi-hop Question Answering Over Knowledge Graph [89.98762327725112]
  Multi-hop Question Answering over Knowledge Graph(KGQA) aims to find the answer entities that are multiple hops away from the topic entities mentioned in a natural language question. We propose UniKGQA, a novel approach for multi-hop KGQA task, by unifying retrieval and reasoning in both model architecture and parameter learning.
  arXiv  Detail & Related papers  (2022-12-02T04:08:09Z)
• Open-domain Question Answering via Chain of Reasoning over Heterogeneous Knowledge [82.5582220249183]
  We propose a novel open-domain question answering (ODQA) framework for answering single/multi-hop questions across heterogeneous knowledge sources. Unlike previous methods that solely rely on the retriever for gathering all evidence in isolation, our intermediary performs a chain of reasoning over the retrieved set. Our system achieves competitive performance on two ODQA datasets, OTT-QA and NQ, against tables and passages from Wikipedia.
  arXiv  Detail & Related papers  (2022-10-22T03:21:32Z)
• Knowledge Base Question Answering by Case-based Reasoning over Subgraphs [81.22050011503933]
  We show that our model answers queries requiring complex reasoning patterns more effectively than existing KG completion algorithms. The proposed model outperforms or performs competitively with state-of-the-art models on several KBQA benchmarks.
  arXiv  Detail & Related papers  (2022-02-22T01:34:35Z)
• Learning Query Expansion over the Nearest Neighbor Graph [94.80212602202518]
  Graph Query Expansion (GQE) is presented, which is learned in a supervised manner and performs aggregation over an extended neighborhood of the query. The technique achieves state-of-the-art results over known benchmarks.
  arXiv  Detail & Related papers  (2021-12-05T19:48:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.