Towards Adaptive Memory-Based Optimization for Enhanced Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2504.05312v3
• Date: Fri, 27 Jun 2025 09:17:10 GMT
• Title: Towards Adaptive Memory-Based Optimization for Enhanced Retrieval-Augmented Generation
• Authors: Qitao Qin, Yucong Luo, Yihang Lu, Zhibo Chu, Xianwei Meng,
• Abstract summary: Retrieval-Augmented Generation (RAG) integrates non-parametric knowledge from external knowledge bases into models.<n>Existing RAG methods struggle with open-domain Question Answering (QA) tasks.<n>We propose Adaptive memory-based optimization for enhanced RAG for open-domain QA tasks.
• Score: 3.294519547931054
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrieval-Augmented Generation (RAG), by integrating non-parametric knowledge from external knowledge bases into models, has emerged as a promising approach to enhancing response accuracy while mitigating factual errors and hallucinations. This method has been widely applied in tasks such as Question Answering (QA). However, existing RAG methods struggle with open-domain QA tasks because they perform independent retrieval operations and directly incorporate the retrieved information into generation without maintaining a summarizing memory or using adaptive retrieval strategies, leading to noise from redundant information and insufficient information integration. To address these challenges, we propose Adaptive memory-based optimization for enhanced RAG (Amber) for open-domain QA tasks, which comprises an Agent-based Memory Updater, an Adaptive Information Collector, and a Multi-granular Content Filter, working together within an iterative memory updating paradigm. Specifically, Amber integrates and optimizes the language model's memory through a multi-agent collaborative approach, ensuring comprehensive knowledge integration from previous retrieval steps. It dynamically adjusts retrieval queries and decides when to stop retrieval based on the accumulated knowledge, enhancing retrieval efficiency and effectiveness. Additionally, it reduces noise by filtering irrelevant content at multiple levels, retaining essential information to improve overall model performance. We conduct extensive experiments on several open-domain QA datasets, and the results demonstrate the superiority and effectiveness of our method and its components. The source code is available \footnote{https://anonymous.4open.science/r/Amber-B203/}.

Related papers

• PAIRS: Parametric-Verified Adaptive Information Retrieval and Selection for Efficient RAG [14.631028226704883]
  We introduce Parametric-verified Adaptive Information Retrieval and Selection (PAIRS)<n>PAIRS integrates parametric and retrieved knowledge to adaptively determine whether to retrieve and how to select external information.<n>We show that PAIRS reduces retrieval costs by around 25% (triggering for only 75% of queries) while still improving accuracy-achieving +1.1% EM and +1.0% F1 over prior baselines.
  arXiv  Detail & Related papers  (2025-08-06T03:33:01Z)
• KARE-RAG: Knowledge-Aware Refinement and Enhancement for RAG [63.82127103851471]
  Retrieval-Augmented Generation (RAG) enables large language models to access broader knowledge sources.<n>We demonstrate that enhancing generative models' capacity to process noisy content is equally critical for robust performance.<n>We present KARE-RAG, which improves knowledge utilization through three key innovations.
  arXiv  Detail & Related papers  (2025-06-03T06:31:17Z)
• ImpRAG: Retrieval-Augmented Generation with Implicit Queries [49.510101132093396]
  ImpRAG is a query-free RAG system that integrates retrieval and generation into a unified model.<n>We show that ImpRAG achieves 3.6-11.5 improvements in exact match scores on unseen tasks with diverse formats.
  arXiv  Detail & Related papers  (2025-06-02T21:38:21Z)
• Towards Multi-Granularity Memory Association and Selection for Long-Term Conversational Agents [73.77930932005354]
  We propose MemGAS, a framework that enhances memory consolidation by constructing multi-granularity association, adaptive selection, and retrieval.<n>MemGAS is based on multi-granularity memory units and employs Gaussian Mixture Models to cluster and associate new memories with historical ones.<n>Experiments on four long-term memory benchmarks demonstrate that MemGAS outperforms state-of-the-art methods on both question answer and retrieval tasks.
  arXiv  Detail & Related papers  (2025-05-26T06:13:07Z)
• Scent of Knowledge: Optimizing Search-Enhanced Reasoning with Information Foraging [7.047640531842663]
  InForage is a reinforcement learning framework that formalizes retrieval-augmented reasoning as a dynamic information-seeking process.<n>We construct a human-guided dataset capturing iterative search and reasoning trajectories for complex, real-world web tasks.<n>These results highlight InForage's effectiveness in building robust, adaptive, and efficient reasoning agents.
  arXiv  Detail & Related papers  (2025-05-14T12:13:38Z)
• Lightweight and Direct Document Relevance Optimization for Generative Information Retrieval [49.669503570350166]
  Generative information retrieval (GenIR) is a promising neural retrieval paradigm that formulates document retrieval as a document identifier (docid) generation task.<n>Existing GenIR models suffer from token-level misalignment, where models trained to predict the next token often fail to capture document-level relevance effectively.<n>We propose direct document relevance optimization (DDRO), which aligns token-level docid generation with document-level relevance estimation through direct optimization via pairwise ranking.
  arXiv  Detail & Related papers  (2025-04-07T15:27:37Z)
• Scaling Test-Time Inference with Policy-Optimized, Dynamic Retrieval-Augmented Generation via KV Caching and Decoding [2.368662284133926]
  We present a framework for enhancing Retrieval-Augmented Generation (RAG) systems through dynamic retrieval strategies and reinforcement fine-tuning.<n>Our framework integrates two complementary techniques: Policy-d RetrievalAugmented Generation (PORAG) and Adaptive Token-Layer Attention Scoring (ATLAS)<n>Our framework reduces hallucinations, strengthens domain-specific reasoning, and achieves significant efficiency and scalability gains over traditional RAG systems.
  arXiv  Detail & Related papers  (2025-04-02T01:16:10Z)
• 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)
• Improving Factuality with Explicit Working Memory [68.39261790277615]
  Large language models can generate factually inaccurate content, a problem known as hallucination.<n>We introduce EWE (Explicit Working Memory), a novel approach that enhances factuality in long-form text generation by integrating a working memory that receives real-time feedback from external resources.
  arXiv  Detail & Related papers  (2024-12-24T00:55:59Z)
• Don't Do RAG: When Cache-Augmented Generation is All You Need for Knowledge Tasks [11.053340674721005]
  Retrieval-augmented generation (RAG) has gained traction as a powerful approach for enhancing language models by integrating external knowledge sources.<n>This paper proposes an alternative paradigm, cache-augmented generation (CAG) that bypasses real-time retrieval.
  arXiv  Detail & Related papers  (2024-12-20T06:58:32Z)
• DeepNote: Note-Centric Deep Retrieval-Augmented Generation [72.70046559930555]
  Retrieval-Augmented Generation (RAG) mitigates factual errors and hallucinations in Large Language Models (LLMs) for question-answering (QA)<n>We develop DeepNote, an adaptive RAG framework that achieves in-depth and robust exploration of knowledge sources through note-centric adaptive retrieval.
  arXiv  Detail & Related papers  (2024-10-11T14:03:29Z)
• 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)

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.