Retrieval-Augmented Generation by Evidence Retroactivity in LLMs

• URL: http://arxiv.org/abs/2501.05475v1
• Date: Tue, 07 Jan 2025 08:57:42 GMT
• Title: Retrieval-Augmented Generation by Evidence Retroactivity in LLMs
• Authors: Liang Xiao, Wen Dai, Shuai Chen, Bin Qin, Chongyang Shi, Haopeng Jing, Tianyu Guo,
• Abstract summary: Retroactive Retrieval-Augmented Generation (RetroRAG) is a novel framework to build a retroactive reasoning paradigm.<n>RetroRAG revises and updates the evidence, redirecting the reasoning chain to the correct direction.<n> Empirical evaluations show that RetroRAG significantly outperforms existing methods.
• Score: 19.122314663040726
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrieval-augmented generation has gained significant attention due to its ability to integrate relevant external knowledge, enhancing the accuracy and reliability of the LLMs' responses. Most of the existing methods apply a dynamic multiple retrieval-generating process, to address multi-hop complex questions by decomposing them into sub-problems. However, these methods rely on an unidirectional forward reasoning paradigm, where errors from insufficient reasoning steps or inherent flaws in current retrieval systems are irreversible, potentially derailing the entire reasoning chain. For the first time, this work introduces Retroactive Retrieval-Augmented Generation (RetroRAG), a novel framework to build a retroactive reasoning paradigm. RetroRAG revises and updates the evidence, redirecting the reasoning chain to the correct direction. RetroRAG constructs an evidence-collation-discovery framework to search, generate, and refine credible evidence. It synthesizes inferential evidence related to the key entities in the question from the existing source knowledge and formulates search queries to uncover additional information. As new evidence is found, RetroRAG continually updates and organizes this information, enhancing its ability to locate further necessary evidence. Paired with an Answerer to generate and evaluate outputs, RetroRAG is capable of refining its reasoning process iteratively until a reliable answer is obtained. Empirical evaluations show that RetroRAG significantly outperforms existing methods.

Related papers

• 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)
• ClueAnchor: Clue-Anchored Knowledge Reasoning Exploration and Optimization for Retrieval-Augmented Generation [82.28147821286709]
  We propose ClueAnchor, a novel framework for enhancing Retrieval-Augmented Generation (RAG)<n>ClueAnchor extracts key clues from retrieved content and generates multiple reasoning paths based on different knowledge configurations.<n>Experiments show that ClueAnchor significantly outperforms prior RAG baselines in reasoning completeness and robustness.
  arXiv  Detail & Related papers  (2025-05-30T09:18:08Z)
• Retrieval-Augmented Generation: A Comprehensive Survey of Architectures, Enhancements, and Robustness Frontiers [0.0]
  Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm to enhance large language models.<n>RAG introduces new challenges in retrieval quality, grounding fidelity, pipeline efficiency, and robustness against noisy or adversarial inputs.<n>This survey aims to consolidate current knowledge in RAG research and serve as a foundation for the next generation of retrieval-augmented language modeling systems.
  arXiv  Detail & Related papers  (2025-05-28T22:57:04Z)
• Resolving Conflicting Evidence in Automated Fact-Checking: A Study on Retrieval-Augmented LLMs [12.923119372847834]
  This paper presents the first systematic evaluation of Retrieval-Augmented Generation (RAG) models for fact-checking.<n>Experiments reveal critical vulnerabilities in state-of-the-art RAG methods, particularly in resolving conflicts stemming from differences in media source credibility.<n>Our results show that effectively incorporating source credibility significantly enhances the ability of RAG models to resolve conflicting evidence and improve fact-checking performance.
  arXiv  Detail & Related papers  (2025-05-23T11:35:03Z)
• Search and Refine During Think: Autonomous Retrieval-Augmented Reasoning of LLMs [25.800565994304847]
  Large language models have demonstrated impressive reasoning capabilities but are inherently limited by their knowledge reservoir.<n>Retrieval-augmented reasoning mitigates this limitation by allowing LLMs to query external resources.<n>We propose AutoRefine, a reinforcement learning framework that adopts a new search-and-refine-during-think'' paradigm.
  arXiv  Detail & Related papers  (2025-05-16T14:11:29Z)
• NeoQA: Evidence-based Question Answering with Generated News Events [53.85274258429368]
  We introduce NeoQA, a benchmark designed to address this issue.<n>We propose our dataset as a new platform for evaluating evidence-based question answering.
  arXiv  Detail & Related papers  (2025-05-09T10:51:29Z)
• AlignRAG: Leveraging Critique Learning for Evidence-Sensitive Retrieval-Augmented Reasoning [61.28113271728859]
  RAG has become a widely adopted paradigm for enabling knowledge-grounded large language models (LLMs)<n>Standard RAG pipelines often fail to ensure that model reasoning remains consistent with the evidence retrieved, leading to factual inconsistencies or unsupported conclusions.<n>In this work, we reinterpret RAG as Retrieval-Augmented Reasoning and identify a central but underexplored problem: textitReasoning Misalignment.
  arXiv  Detail & Related papers  (2025-04-21T04:56:47Z)
• CDF-RAG: Causal Dynamic Feedback for Adaptive Retrieval-Augmented Generation [3.8808821719659763]
  We introduce Causal Dynamic Feedback for Adaptive Retrieval-Augmented Generation (CDF-RAG) CDF-RAG iteratively refines queries, retrieves structured causal graphs, and enables multi-hop causal reasoning across interconnected knowledge sources. We evaluate CDF-RAG on four diverse datasets, demonstrating its ability to improve response accuracy and causal correctness over existing RAG-based methods.
  arXiv  Detail & Related papers  (2025-04-17T01:15:13Z)
• 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) By iteratively decomposing queries, DeepRAG dynamically determines whether to retrieve external knowledge or rely on parametric reasoning at each step. 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)
• Chain-of-Retrieval Augmented Generation [72.06205327186069]
  This paper introduces an approach for training o1-like RAG models that retrieve and reason over relevant information step by step before generating the final answer. Our proposed method, CoRAG, allows the model to dynamically reformulate the query based on the evolving state.
  arXiv  Detail & Related papers  (2025-01-24T09:12:52Z)
• RAG-Star: Enhancing Deliberative Reasoning with Retrieval Augmented Verification and Refinement [85.08223786819532]
  Existing large language models (LLMs) show exceptional problem-solving capabilities but might struggle with complex reasoning tasks.<n>We propose textbfRAG-Star, a novel RAG approach that integrates retrieved information to guide the tree-based deliberative reasoning process.<n>Our experiments involving Llama-3.1-8B-Instruct and GPT-4o demonstrate that RAG-Star significantly outperforms previous RAG and reasoning methods.
  arXiv  Detail & Related papers  (2024-12-17T13:05:36Z)
• Unanswerability Evaluation for Retreival Augmented Generation [74.3022365715597]
  UAEval4RAG is a framework designed to evaluate whether RAG systems can handle unanswerable queries effectively.<n>We define a taxonomy with six unanswerable categories, and UAEval4RAG automatically synthesizes diverse and challenging queries.
  arXiv  Detail & Related papers  (2024-12-16T19:11:55Z)
• Atomic Fact Decomposition Helps Attributed Question Answering [30.75332718824254]
  Attributed Question Answering (AQA) aims to provide both a trustworthy answer and a reliable attribution report for a question. This paper proposes an Atomic fact decomposition-based Retrieval and Editing framework. It decomposes the generated long-form answers into molecular clauses and atomic facts by the instruction-tuned LLMs.
  arXiv  Detail & Related papers  (2024-10-22T05:25:54Z)
• Retriever-and-Memory: Towards Adaptive Note-Enhanced Retrieval-Augmented Generation [72.70046559930555]
  We propose a generic RAG approach called Adaptive Note-Enhanced RAG (Adaptive-Note) for complex QA tasks. Specifically, Adaptive-Note introduces an overarching view of knowledge growth, iteratively gathering new information in the form of notes. In addition, we employ an adaptive, note-based stop-exploration strategy to decide "what to retrieve and when to stop" to encourage sufficient knowledge exploration.
  arXiv  Detail & Related papers  (2024-10-11T14:03:29Z)
• Retrieving, Rethinking and Revising: The Chain-of-Verification Can Improve Retrieval Augmented Generation [38.80878966092216]
  Recent Retrieval Augmented Generation (RAG) aims to enhance Large Language Models (LLMs) We propose the chain-of-verification (CoV-RAG) to enhance the external retrieval correctness and internal generation consistency.
  arXiv  Detail & Related papers  (2024-10-08T08:34:54Z)
• RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generation [42.82192656794179]
  Large Language Models (LLMs) exhibit remarkable capabilities but are prone to generating inaccurate or hallucinatory responses. This limitation stems from their reliance on vast pretraining datasets, making them susceptible to errors in unseen scenarios. Retrieval-Augmented Generation (RAG) addresses this by incorporating external, relevant documents into the response generation process.
  arXiv  Detail & Related papers  (2024-03-31T08:58:54Z)
• Corrective Retrieval Augmented Generation [36.04062963574603]
  Retrieval-augmented generation (RAG) relies heavily on relevance of retrieved documents, raising concerns about how the model behaves if retrieval goes wrong. We propose the Corrective Retrieval Augmented Generation (CRAG) to improve the robustness of generation. CRAG is plug-and-play and can be seamlessly coupled with various RAG-based approaches.
  arXiv  Detail & Related papers  (2024-01-29T04:36:39Z)
• 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)
• Large Language Models for Information Retrieval: A Survey [58.30439850203101]
  Information retrieval has evolved from term-based methods to its integration with advanced neural models. Recent research has sought to leverage large language models (LLMs) to improve IR systems. We delve into the confluence of LLMs and IR systems, including crucial aspects such as query rewriters, retrievers, rerankers, and readers.
  arXiv  Detail & Related papers  (2023-08-14T12:47:22Z)
• REM-Net: Recursive Erasure Memory Network for Commonsense Evidence Refinement [130.8875535449478]
  REM-Net is equipped with a module to refine the evidence by erasing the low-quality evidence that does not explain the question answering. Instead of retrieving evidence from existing knowledge bases, REM-Net leverages a pre-trained generative model to generate candidate evidence customized for the question. The results demonstrate the performance of REM-Net and show that the refined evidence is explainable.
  arXiv  Detail & Related papers  (2020-12-24T10:07:32Z)

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.