PropRAG: Guiding Retrieval with Beam Search over Proposition Paths

• URL: http://arxiv.org/abs/2504.18070v2
• Date: Fri, 03 Oct 2025 08:26:29 GMT
• Title: PropRAG: Guiding Retrieval with Beam Search over Proposition Paths
• Authors: Jingjin Wang, Jiawei Han,
• Abstract summary: PropRAG is a novel RAG framework that shifts from triples to context-rich propositions.<n>PropRAG achieves Recall@5 and F1 scores on 2Wiki, HotpotQA, and MuSiQue.
• Score: 15.346744525284604
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Retrieval Augmented Generation (RAG) has become the standard approach for equipping Large Language Models (LLMs) with up-to-date knowledge. However, standard RAG, relying on independent passage retrieval, often fails to capture the interconnected nature of information required for complex, multi-hop reasoning. While structured RAG methods attempt to address this using knowledge graphs built from triples, we argue that the inherent context loss of triples (context collapse) limits the fidelity of the knowledge representation. We introduce PropRAG, a novel RAG framework that shifts from triples to context-rich propositions and introduces an efficient, LLM-free online beam search over proposition paths to discover multi-step reasoning chains. By coupling a higher-fidelity knowledge representation with explicit path discovery, PropRAG achieves state-of-the-art zero-shot Recall@5 and F1 scores on 2Wiki, HotpotQA, and MuSiQue, advancing non-parametric knowledge integration by improving evidence retrieval through richer representation and efficient reasoning path discovery.

Related papers

• HELP: HyperNode Expansion and Logical Path-Guided Evidence Localization for Accurate and Efficient GraphRAG [53.30561659838455]
  Large Language Models (LLMs) often struggle with inherent knowledge boundaries and hallucinations.<n>Retrieval-Augmented Generation (RAG) frequently overlooks structural interdependencies essential for multi-hop reasoning.<n>Help achieves competitive performance across multiple simple and multi-hop QA benchmarks and up to a 28.8$times$ speedup over leading Graph-based RAG baselines.
  arXiv  Detail & Related papers  (2026-02-24T14:05:29Z)
• Multi-hop Reasoning via Early Knowledge Alignment [68.28168992785896]
  Early Knowledge Alignment (EKA) aims to align Large Language Models with contextually relevant retrieved knowledge.<n>EKA significantly improves retrieval precision, reduces cascading errors, and enhances both performance and efficiency.<n>EKA proves effective as a versatile, training-free inference strategy that scales seamlessly to large models.
  arXiv  Detail & Related papers  (2025-12-23T08:14:44Z)
• GRIL: Knowledge Graph Retrieval-Integrated Learning with Large Language Models [59.72897499248909]
  We propose a novel graph retriever trained end-to-end with Large Language Models (LLMs)<n>Within the extracted subgraph, structural knowledge and semantic features are encoded via soft tokens and the verbalized graph, respectively, which are infused into the LLM together.<n>Our approach consistently achieves state-of-the-art performance, validating the strength of joint graph-LLM optimization for complex reasoning tasks.
  arXiv  Detail & Related papers  (2025-09-20T02:38:00Z)
• LeanRAG: Knowledge-Graph-Based Generation with Semantic Aggregation and Hierarchical Retrieval [10.566901995776025]
  LeanRAG is a framework that combines knowledge aggregation and retrieval strategies.<n>It can mitigate the substantial overhead associated with path retrieval on graphs and minimizes redundant information retrieval.
  arXiv  Detail & Related papers  (2025-08-14T06:47:18Z)
• Beyond Chunks and Graphs: Retrieval-Augmented Generation through Triplet-Driven Thinking [31.73448933991891]
  Retrieval-augmented generation (RAG) is critical for reducing hallucinations and incorporating external knowledge into Large Language Models (LLMs)<n>We propose T$2$RAG, a novel framework that operates on a simple, graph-free knowledge base of atomic triplets.<n> Empirical results show that T$2$RAG significantly outperforms state-of-the-art multi-round and Graph RAG methods.
  arXiv  Detail & Related papers  (2025-08-04T13:50:44Z)
• Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities [43.88784275673178]
  Retrieval-Augmented Generation (RAG) shows impressive performance by supplementing and substituting parametric knowledge in Large Language Models (LLMs)<n>We show how triplets located in the gold reasoning path and their variants are used to construct partially relevant knowledge by removing the path that contains the answer.<n>Our awakening-based approach demonstrates greater efficacy in practical applications, outperforms traditional methods that rely on embedding-based similarity.
  arXiv  Detail & Related papers  (2025-08-02T09:54:46Z)
• PrismRAG: Boosting RAG Factuality with Distractor Resilience and Strategized Reasoning [57.89188317734747]
  PrismRAG trains the model with distractor-aware QA pairs mixing gold evidence with subtle distractor passages.<n>It instills reasoning-centric habits that make the LLM plan, rationalize, and synthesize without relying on extensive human engineered instructions.
  arXiv  Detail & Related papers  (2025-07-25T00:15:31Z)
• Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs [69.10441885629787]
  Retrieval-Augmented Generation (RAG) lifts the factuality of Large Language Models (LLMs) by injecting external knowledge.<n>It falls short on problems that demand multi-step inference; conversely, purely reasoning-oriented approaches often hallucinate or mis-ground facts.<n>This survey synthesizes both strands under a unified reasoning-retrieval perspective.
  arXiv  Detail & Related papers  (2025-07-13T03:29:41Z)
• Clue-RAG: Towards Accurate and Cost-Efficient Graph-based RAG via Multi-Partite Graph and Query-Driven Iterative Retrieval [7.542076325904203]
  Retrieval-Augmented Generation (RAG) addresses the limitation by incorporating external information, often from graph-structured data.<n>We propose Clue-RAG, a novel approach that introduces a multi-partite graph index and a query-driven iterative retrieval strategy.<n>Experiments on three QA benchmarks show that Clue-RAG significantly outperforms state-of-the-art baselines.
  arXiv  Detail & Related papers  (2025-07-11T09:36:45Z)
• RAG+: Enhancing Retrieval-Augmented Generation with Application-Aware Reasoning [18.233905463518553]
  We introduce RAG+, a principled and modular extension that explicitly incorporates application-aware reasoning into the RAG pipeline.<n>RAG+ constructs a dual corpus consisting of knowledge and aligned application examples, created either manually or automatically, and retrieves both jointly during inference.<n> Experiments across mathematical, legal, and medical domains, conducted on multiple models, demonstrate that RAG+ consistently outperforms standard RAG variants.
  arXiv  Detail & Related papers  (2025-06-13T08:06:49Z)
• Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering [75.12322966980003]
  Large Language Models (LLMs) have shown strong inductive reasoning ability across various domains.<n>Most existing RAG pipelines rely on unstructured text, limiting interpretability and structured reasoning.<n>Recent studies have explored integrating knowledge graphs with LLMs for knowledge graph question answering.<n>We propose RAPL, a novel framework for efficient and effective graph retrieval in KGQA.
  arXiv  Detail & Related papers  (2025-06-11T12:03:52Z)
• KnowTrace: Bootstrapping Iterative Retrieval-Augmented Generation with Structured Knowledge Tracing [64.38243807002878]
  We present KnowTrace, an elegant RAG framework to mitigate the context overload in large language models.<n>KnowTrace autonomously traces out desired knowledge triplets to organize a specific knowledge graph relevant to the input question.<n>It consistently surpasses existing methods across three multi-hop question answering benchmarks.
  arXiv  Detail & Related papers  (2025-05-26T17:22:20Z)
• R3-RAG: Learning Step-by-Step Reasoning and Retrieval for LLMs via Reinforcement Learning [62.742230250513025]
  Retrieval-Augmented Generation (RAG) integrates external knowledge with Large Language Models (LLMs) to enhance factual correctness and hallucination.<n>We propose $textbfR3-RAG$, which uses $textbfR$einforcement learning to make the LLM learn how to $textbfR$eason and $textbfR$etrieve step by step, thus retrieving comprehensive external knowledge and leading to correct answers.
  arXiv  Detail & Related papers  (2025-05-26T12:25:37Z)
• AlignRAG: An Adaptable Framework for Resolving Misalignments in Retrieval-Aware Reasoning of RAG [61.28113271728859]
  Retrieval-augmented generation (RAG) has emerged as a foundational paradigm for knowledge-grounded text generation.<n>Existing RAG pipelines often fail to ensure that the reasoning trajectories align with the evidential constraints imposed by retrieved content.<n>We propose AlignRAG, a novel test-time framework that mitigates reasoning misalignment through iterative Critique-Driven Alignment steps.
  arXiv  Detail & Related papers  (2025-04-21T04:56:47Z)
• Self-Routing RAG: Binding Selective Retrieval with Knowledge Verbalization [97.72503890388866]
  We propose Self-Routing RAG (SR-RAG), a novel framework that binds selective retrieval with knowledge verbalization.<n>SR-RAG enables an LLM to dynamically decide between external retrieval and verbalizing its own parametric knowledge.<n>We introduce dynamic knowledge source inference via nearest neighbor search to improve the accuracy of knowledge source decision.
  arXiv  Detail & Related papers  (2025-04-01T17:59:30Z)
• HopRAG: Multi-Hop Reasoning for Logic-Aware Retrieval-Augmented Generation [28.69822159828129]
  We propose HopRAG, a novel RAG framework that augments retrieval with logical reasoning.<n>During indexing, HopRAG constructs a passage graph, with text chunks as vertices and logical connections established via LLM-generated pseudo-queries as edges.<n>During retrieval, it employs a retrieve-reason-prune mechanism, starting with lexically or semantically similar passages.<n>Experiments demonstrate HopRAG's superiority, achieving 76.78% higher answer accuracy and 65.07% improved retrieval F1 score compared to conventional methods.
  arXiv  Detail & Related papers  (2025-02-18T02:24:42Z)
• LaRA: Benchmarking Retrieval-Augmented Generation and Long-Context LLMs -- No Silver Bullet for LC or RAG Routing [70.35888047551643]
  We present LaRA, a novel benchmark specifically designed to rigorously compare RAG and LC LLMs.<n>LaRA encompasses 2326 test cases across four practical QA task categories and three types of naturally occurring long texts.<n>We find that the optimal choice between RAG and LC depends on a complex interplay of factors, including the model's parameter size, long-text capabilities, context length, task type, and the characteristics of the retrieved chunks.
  arXiv  Detail & Related papers  (2025-02-14T08:04:22Z)
• DeepRAG: Thinking to Retrieval 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 while improving answer accuracy by 21.99%, demonstrating its effectiveness in optimizing retrieval-augmented reasoning.
  arXiv  Detail & Related papers  (2025-02-03T08:22:45Z)
• SFR-RAG: Towards Contextually Faithful LLMs [57.666165819196486]
  Retrieval Augmented Generation (RAG) is a paradigm that integrates external contextual information with large language models (LLMs) to enhance factual accuracy and relevance. We introduce SFR-RAG, a small LLM that is instruction-textual with an emphasis on context-grounded generation and hallucination. We also present ConBench, a new evaluation framework compiling multiple popular and diverse RAG benchmarks.
  arXiv  Detail & Related papers  (2024-09-16T01:08:18Z)
• W-RAG: Weakly Supervised Dense Retrieval in RAG for Open-domain Question Answering [28.79851078451609]
  We propose W-RAG, a method that draws weak training signals from the downstream task and fine-tunes the retriever to prioritize passages that most benefit the task. We conduct comprehensive experiments across four publicly available OpenQA datasets to demonstrate that our approach enhances both retrieval and OpenQA performance.
  arXiv  Detail & Related papers  (2024-08-15T22:34:44Z)
• Prompt Perturbation in Retrieval-Augmented Generation based Large Language Models [9.688626139309013]
  Retrieval-Augmented Generation is considered as a means to improve the trustworthiness of text generation from large language models. In this work, we find that the insertion of even a short prefix to the prompt leads to the generation of outputs far away from factually correct answers. We introduce a novel optimization technique called Gradient Guided Prompt Perturbation.
  arXiv  Detail & Related papers  (2024-02-11T12:25:41Z)
• Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection [74.51523859064802]
  We introduce a new framework called Self-Reflective Retrieval-Augmented Generation (Self-RAG) Self-RAG enhances an LM's quality and factuality through retrieval and self-reflection. It significantly outperforms state-of-the-art LLMs and retrieval-augmented models on a diverse set of tasks.
  arXiv  Detail & Related papers  (2023-10-17T18:18:32Z)

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.