Resource-Friendly Dynamic Enhancement Chain for Multi-Hop Question Answering

• URL: http://arxiv.org/abs/2506.17692v1
• Date: Sat, 21 Jun 2025 11:55:27 GMT
• Title: Resource-Friendly Dynamic Enhancement Chain for Multi-Hop Question Answering
• Authors: Binquan Ji, Haibo Luo, Yifei Lu, Lei Hei, Jiaqi Wang, Tingjing Liao, Lingyu Wang, Shichao Wang, Feiliang Ren,
• Abstract summary: This work proposes a novel framework called DEC (Dynamic Enhancement Chain)<n> DEC first decomposes complex questions into logically coherent subquestions to form a hallucination-free reasoning chain.<n>It then iteratively refines these subquestions through context-aware rewriting to generate effective query formulations.
• Score: 21.077964610022313
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Knowledge-intensive multi-hop question answering (QA) tasks, which require integrating evidence from multiple sources to address complex queries, often necessitate multiple rounds of retrieval and iterative generation by large language models (LLMs). However, incorporating many documents and extended contexts poses challenges -such as hallucinations and semantic drift-for lightweight LLMs with fewer parameters. This work proposes a novel framework called DEC (Dynamic Enhancement Chain). DEC first decomposes complex questions into logically coherent subquestions to form a hallucination-free reasoning chain. It then iteratively refines these subquestions through context-aware rewriting to generate effective query formulations. For retrieval, we introduce a lightweight discriminative keyword extraction module that leverages extracted keywords to achieve targeted, precise document recall with relatively low computational overhead. Extensive experiments on three multi-hop QA datasets demonstrate that DEC performs on par with or surpasses state-of-the-art benchmarks while significantly reducing token consumption. Notably, our approach attains state-of-the-art results on models with 8B parameters, showcasing its effectiveness in various scenarios, particularly in resource-constrained environments.

Related papers

• Tagging-Augmented Generation: Assisting Language Models in Finding Intricate Knowledge In Long Contexts [6.14987671066697]
  In this paper, we propose Tagging-Augmented Generation (TAG) as a lightweight data augmentation strategy for long-context scenarios.<n>We validate our hypothesis by augmenting two challenging and directly relevant question-answering benchmarks -- NoLima and NovelQA.<n>We show that tagging the context or even just adding tag definitions into QA prompts leads to consistent performance gains over the baseline.
  arXiv  Detail & Related papers  (2025-10-27T03:23:25Z)
• Enhancing Retrieval-Augmented Generation with Two-Stage Retrieval: FlashRank Reranking and Query Expansion [0.0]
  RAG couples a retriever with a large language model (LLM) to ground generated responses in external evidence.<n>We propose a two-stage retrieval pipeline that integrates LLM-driven query expansion to improve candidate recall.<n>FlashRank is a fast marginal-utility reranker that dynamically selects an optimal subset of evidence under a token budget.
  arXiv  Detail & Related papers  (2025-10-17T15:08:17Z)
• Reasoning-enhanced Query Understanding through Decomposition and Interpretation [87.56450566014625]
  ReDI is a Reasoning-enhanced approach for query understanding through Decomposition and Interpretation.<n>We compiled a large-scale dataset of real-world complex queries from a major search engine.<n> Experiments on BRIGHT and BEIR demonstrate that ReDI consistently surpasses strong baselines in both sparse and dense retrieval paradigms.
  arXiv  Detail & Related papers  (2025-09-08T10:58:42Z)
• HANRAG: Heuristic Accurate Noise-resistant Retrieval-Augmented Generation for Multi-hop Question Answering [25.01360941843264]
  HANRAG is a novel framework designed to efficiently tackle problems of varying complexity.<n> Driven by a powerful revelator, HANRAG routes queries, decomposes them into sub-queries, and filters noise from retrieved documents.<n>Results demonstrate that our framework obtains superior performance in both single-hop and multi-hop question-answering tasks.
  arXiv  Detail & Related papers  (2025-09-08T06:22:38Z)
• Decompositional Reasoning for Graph Retrieval with Large Language Models [1.034893617526558]
  Large Language Models (LLMs) excel at many NLP tasks, but struggle with multi-hop reasoning and factual consistency.<n>We propose a novel retrieval approach that integrates textual knowledge graphs into the LLM reasoning process via query decomposition.<n>Our method decomposes complex questions into sub-questions, retrieves relevant textual subgraphs, and composes a question-specific knowledge graph to guide answer generation.
  arXiv  Detail & Related papers  (2025-06-16T11:44:28Z)
• Optimizing Question Semantic Space for Dynamic Retrieval-Augmented Multi-hop Question Answering [28.09833765246606]
  Q-DREAM consists of three key modules: (1) the Question Decomposition Module (QDM), which decomposes interdependent subquestions; (2) the Subquestion Dependency Module (SDOM), which models the relations of subquestions for better understanding; and (3) the Dynamic Passage Retrieval Module (DPRM), which aligns subquestions with relevant passages by optimizing the semantic embeddings.<n> Experimental results across various benchmarks demonstrate that Q-DREAM significantly outperforms existing RAG methods, achieving state-of-the-art performance in both in-domain and out-of-domain settings.
  arXiv  Detail & Related papers  (2025-05-31T09:57:07Z)
• Divide by Question, Conquer by Agent: SPLIT-RAG with Question-Driven Graph Partitioning [62.640169289390535]
  SPLIT-RAG is a multi-agent RAG framework that addresses the limitations with question-driven semantic graph partitioning and collaborative subgraph retrieval.<n>The innovative framework first create Semantic Partitioning of Linked Information, then use the Type-Specialized knowledge base to achieve Multi-Agent RAG.<n>The attribute-aware graph segmentation manages to divide knowledge graphs into semantically coherent subgraphs, ensuring subgraphs align with different query types.<n>A hierarchical merging module resolves inconsistencies across subgraph-derived answers through logical verifications.
  arXiv  Detail & Related papers  (2025-05-20T06:44:34Z)
• MultiConIR: Towards multi-condition Information Retrieval [57.6405602406446]
  We introduce MultiConIR, the first benchmark designed to evaluate retrieval models in multi-condition scenarios.<n>We propose three tasks to assess retrieval and reranking models on multi-condition robustness, monotonic relevance ranking, and query format sensitivity.
  arXiv  Detail & Related papers  (2025-03-11T05:02:03Z)
• GRITHopper: Decomposition-Free Multi-Hop Dense Retrieval [52.47514434103737]
  We introduce GRITHopper-7B, a novel multi-hop dense retrieval model that achieves state-of-the-art performance.<n> GRITHopper combines generative and representational instruction tuning by integrating causal language modeling with dense retrieval training.<n>We find that incorporating additional context after the retrieval process, referred to as post-retrieval language modeling, enhances dense retrieval performance.
  arXiv  Detail & Related papers  (2025-03-10T16:42:48Z)
• BRIEF: Bridging Retrieval and Inference for Multi-hop Reasoning via Compression [91.23933111083389]
  Retrieval-augmented generation (RAG) can supplement large language models (LLMs) by integrating external knowledge.<n>This paper presents BRIEF, a lightweight approach that performs query-aware multi-hop reasoning.<n>Based on our synthetic data built entirely by open-source models, BRIEF generates more concise summaries.
  arXiv  Detail & Related papers  (2024-10-20T04:24:16Z)
• Retrieve, Summarize, Plan: Advancing Multi-hop Question Answering with an Iterative Approach [6.549143816134531]
  We propose a novel iterative RAG method called ReSP, equipped with a dual-function summarizer.<n> Experimental results on the multi-hop question-answering HotpotQA and 2WikiMultihopQA demonstrate that our method significantly outperforms the state-of-the-art.
  arXiv  Detail & Related papers  (2024-07-18T02:19:00Z)
• Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity [59.57065228857247]
  Retrieval-augmented Large Language Models (LLMs) have emerged as a promising approach to enhancing response accuracy in several tasks, such as Question-Answering (QA) We propose a novel adaptive QA framework, that can dynamically select the most suitable strategy for (retrieval-augmented) LLMs based on the query complexity. We validate our model on a set of open-domain QA datasets, covering multiple query complexities, and show that ours enhances the overall efficiency and accuracy of QA systems.
  arXiv  Detail & Related papers  (2024-03-21T13:52:30Z)
• SEMQA: Semi-Extractive Multi-Source Question Answering [94.04430035121136]
  We introduce a new QA task for answering multi-answer questions by summarizing multiple diverse sources in a semi-extractive fashion. We create the first dataset of this kind, QuoteSum, with human-written semi-extractive answers to natural and generated questions.
  arXiv  Detail & Related papers  (2023-11-08T18:46:32Z)
• Enhancing Multi-modal and Multi-hop Question Answering via Structured Knowledge and Unified Retrieval-Generation [33.56304858796142]
  Multi-modal multi-hop question answering involves answering a question by reasoning over multiple input sources from different modalities. Existing methods often retrieve evidences separately and then use a language model to generate an answer based on the retrieved evidences. We propose a Structured Knowledge and Unified Retrieval-Generation (RG) approach to address these issues.
  arXiv  Detail & Related papers  (2022-12-16T18:12:04Z)
• 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)

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.