Memory-Aware and Uncertainty-Guided Retrieval for Multi-Hop Question Answering

• URL: http://arxiv.org/abs/2503.23095v1
• Date: Sat, 29 Mar 2025 14:27:02 GMT
• Title: Memory-Aware and Uncertainty-Guided Retrieval for Multi-Hop Question Answering
• Authors: Yuelyu Ji, Rui Meng, Zhuochun Li, Daqing He,
• Abstract summary: Multi-hop question answering requires models to retrieve and reason over multiple pieces of evidence.<n>Existing methods often suffer from two key limitations: fixed or overly frequent retrieval steps, and ineffective use of previously retrieved knowledge.<n>We propose MIND, a framework that addresses these challenges through: (i) prompt-based entity extraction to identify reasoning-relevant elements, (ii) dynamic retrieval triggering based on token-level entropy and attention signals, and (iii) memory-aware filtering.
• Score: 11.756344944226495
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-hop question answering (QA) requires models to retrieve and reason over multiple pieces of evidence. While Retrieval-Augmented Generation (RAG) has made progress in this area, existing methods often suffer from two key limitations: (1) fixed or overly frequent retrieval steps, and (2) ineffective use of previously retrieved knowledge. We propose MIND (Memory-Informed and INteractive Dynamic RAG), a framework that addresses these challenges through: (i) prompt-based entity extraction to identify reasoning-relevant elements, (ii) dynamic retrieval triggering based on token-level entropy and attention signals, and (iii) memory-aware filtering, which stores high-confidence facts across reasoning steps to enable consistent multi-hop generation.

Related papers

• Resource-Friendly Dynamic Enhancement Chain for Multi-Hop Question Answering [21.077964610022313]
  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.
  arXiv  Detail & Related papers  (2025-06-21T11:55:27Z)
• 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)
• DualRAG: A Dual-Process Approach to Integrate Reasoning and Retrieval for Multi-Hop Question Answering [45.205396863347964]
  Multi-Hop Question Answering (MHQA) tasks pose challenges in orchestrating multi-step reasoning across diverse knowledge domains. We propose DualRAG, a synergistic dual-process framework that seamlessly integrates reasoning and retrieval.
  arXiv  Detail & Related papers  (2025-04-25T10:43:53Z)
• DeepRAG: Thinking to Retrieve Step by Step for Large Language Models [92.87532210660456]
  We propose DeepRAG, a framework that models retrieval-augmented reasoning as a Markov Decision Process (MDP)<n>By iteratively decomposing queries, DeepRAG dynamically determines whether to retrieve external knowledge or rely on parametric reasoning at each step.<n> Experiments show that DeepRAG improves retrieval efficiency and boosts answer accuracy by 26.4%, demonstrating its effectiveness in enhancing retrieval-augmented reasoning.
  arXiv  Detail & Related papers  (2025-02-03T08:22:45Z)
• Benchmarking Multimodal Retrieval Augmented Generation with Dynamic VQA Dataset and Self-adaptive Planning Agent [92.57125498367907]
  Multimodal Retrieval Augmented Generation (mRAG) plays an important role in mitigating the "hallucination" issue inherent in multimodal large language models (MLLMs)<n>We propose the first self-adaptive planning agent for multimodal retrieval, OmniSearch.
  arXiv  Detail & Related papers  (2024-11-05T09:27:21Z)
• EfficientRAG: Efficient Retriever for Multi-Hop Question Answering [52.64500643247252]
  We introduce EfficientRAG, an efficient retriever for multi-hop question answering. Experimental results demonstrate that EfficientRAG surpasses existing RAG methods on three open-domain multi-hop question-answering datasets.
  arXiv  Detail & Related papers  (2024-08-08T06:57:49Z)
• 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)
• Progressive Evidence Refinement for Open-domain Multimodal Retrieval Question Answering [20.59485758381809]
  Current multimodal retrieval question-answering models face two main challenges. utilizing compressed evidence features as input to the model results in the loss of fine-grained information within the evidence. We propose a two-stage framework for evidence retrieval and question-answering to alleviate these issues.
  arXiv  Detail & Related papers  (2023-10-15T01:18:39Z)
• Modeling Multi-hop Question Answering as Single Sequence Prediction [88.72621430714985]
  We propose a simple generative approach (PathFid) that extends the task beyond just answer generation. PathFid explicitly models the reasoning process to resolve the answer for multi-hop questions. Our experiments demonstrate that PathFid leads to strong performance gains on two multi-hop QA datasets.
  arXiv  Detail & Related papers  (2022-05-18T21:57:59Z)
• Adaptive Information Seeking for Open-Domain Question Answering [61.39330982757494]
  We propose a novel adaptive information-seeking strategy for open-domain question answering, namely AISO. According to the learned policy, AISO could adaptively select a proper retrieval action to seek the missing evidence at each step. AISO outperforms all baseline methods with predefined strategies in terms of both retrieval and answer evaluations.
  arXiv  Detail & Related papers  (2021-09-14T15:08:13Z)
• Memory Augmented Sequential Paragraph Retrieval for Multi-hop Question Answering [32.69969157825044]
  We propose a new architecture that models paragraphs as sequential data and considers multi-hop information retrieval as a kind of sequence labeling task. We evaluate our method on both full wiki and distractor subtask of HotpotQA, a public textual multi-hop QA dataset.
  arXiv  Detail & Related papers  (2021-02-07T08:15:51Z)
• Answering Any-hop Open-domain Questions with Iterative Document Reranking [62.76025579681472]
  We propose a unified QA framework to answer any-hop open-domain questions. Our method consistently achieves performance comparable to or better than the state-of-the-art on both single-hop and multi-hop open-domain QA datasets.
  arXiv  Detail & Related papers  (2020-09-16T04:31:38Z)
• Multi-Stage Conversational Passage Retrieval: An Approach to Fusing Term Importance Estimation and Neural Query Rewriting [56.268862325167575]
  We tackle conversational passage retrieval (ConvPR) with query reformulation integrated into a multi-stage ad-hoc IR system. We propose two conversational query reformulation (CQR) methods: (1) term importance estimation and (2) neural query rewriting. For the former, we expand conversational queries using important terms extracted from the conversational context with frequency-based signals. For the latter, we reformulate conversational queries into natural, standalone, human-understandable queries with a pretrained sequence-tosequence model.
  arXiv  Detail & Related papers  (2020-05-05T14:30:20Z)

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.