KARE-RAG: Knowledge-Aware Refinement and Enhancement for RAG

• URL: http://arxiv.org/abs/2506.02503v1
• Date: Tue, 03 Jun 2025 06:31:17 GMT
• Title: KARE-RAG: Knowledge-Aware Refinement and Enhancement for RAG
• Authors: Yongjian Li, HaoCheng Chu, Yukun Yan, Zhenghao Liu, Shi Yu, Zheni Zeng, Ruobing Wang, Sen Song, Zhiyuan Liu, Maosong Sun,
• Abstract summary: 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.
• Score: 63.82127103851471
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrieval-Augmented Generation (RAG) enables large language models (LLMs) to access broader knowledge sources, yet factual inconsistencies persist due to noise in retrieved documents-even with advanced retrieval methods. We demonstrate that enhancing generative models' capacity to process noisy content is equally critical for robust performance. In this paper, we present KARE-RAG (Knowledge-Aware Refinement and Enhancement for RAG), which improves knowledge utilization through three key innovations: (1) structured knowledge representations that facilitate error detection during training, (2) Dense Direct Preference Optimization (DDPO)-a refined training objective that prioritizes correction of critical errors, and (3) a contrastive data generation pipeline that maintains semantic consistency while rectifying factual inaccuracies. Experiments show our method significantly enhances standard RAG pipelines across model scales, improving both in-domain and out-of-domain task performance without compromising general capabilities. Notably, these gains are achieved with modest training data, suggesting data-efficient optimization is possible through targeted learning strategies. Our findings establish a new direction for RAG improvement: by improving how models learn to process retrieved content, we can enhance performance across diverse inference paradigms. All data and code will be publicly available on Github.

Related papers

• HIRAG: Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation [9.175609521889266]
  We introduce our new RAG instruction fine-tuning method, Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation (HIRAG)<n>This method enhances the model's open-book examination capability by utilizing multi-level progressive chain-of-thought.<n>Experiments show that the HIRAG training strategy significantly improves the model's performance on datasets such as RGB, PopQA, MuSiQue, HotpotQA, and PubmedQA.
  arXiv  Detail & Related papers  (2025-07-08T06:53:28Z)
• Exploring Generalized Gait Recognition: Reducing Redundancy and Noise within Indoor and Outdoor Datasets [24.242460774158463]
  Generalized gait recognition aims to achieve robust performance across diverse domains.<n>Mixed-dataset training is widely used to enhance generalization.<n>We propose a unified framework that systematically improves cross-domain gait recognition.
  arXiv  Detail & Related papers  (2025-05-21T06:46:09Z)
• Effective and Transparent RAG: Adaptive-Reward Reinforcement Learning for Decision Traceability [16.87554947089102]
  We propose ARENA, a transparent RAG generator framework trained via reinforcement learning (RL) with our proposed rewards.<n>Based on the structured generation and adaptive reward calculation, our RL-based training enables the model to identify key evidence, perform structured reasoning, and generate answers with interpretable decision traces.
  arXiv  Detail & Related papers  (2025-05-19T15:40:29Z)
• Enhancing knowledge retention for continual learning with domain-specific adapters and features gating [4.637185817866919]
  Continual learning empowers models to learn from a continuous stream of data while preserving previously acquired knowledge.<n>We propose a new approach that integrates adapters within the self-attention mechanisms of Vision Transformers to enhance knowledge retention when sequentially adding datasets from different domains.
  arXiv  Detail & Related papers  (2025-04-11T15:20:08Z)
• 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)
• Review, Refine, Repeat: Understanding Iterative Decoding of AI Agents with Dynamic Evaluation and Selection [71.92083784393418]
  Inference-time methods such as Best-of-N (BON) sampling offer a simple yet effective alternative to improve performance.<n>We propose Iterative Agent Decoding (IAD) which combines iterative refinement with dynamic candidate evaluation and selection guided by a verifier.
  arXiv  Detail & Related papers  (2025-04-02T17:40:47Z)
• What Really Matters for Learning-based LiDAR-Camera Calibration [50.2608502974106]
  This paper revisits the development of learning-based LiDAR-Camera calibration.<n>We identify the critical limitations of regression-based methods with the widely used data generation pipeline.<n>We also investigate how the input data format and preprocessing operations impact network performance.
  arXiv  Detail & Related papers  (2025-01-28T14:12:32Z)
• Retrieval-Augmented Generation for Large Language Models: A Survey [17.82361213043507]
  Large Language Models (LLMs) showcase impressive capabilities but encounter challenges like hallucination. Retrieval-Augmented Generation (RAG) has emerged as a promising solution by incorporating knowledge from external databases.
  arXiv  Detail & Related papers  (2023-12-18T07:47:33Z)
• Back to Basics: A Simple Recipe for Improving Out-of-Domain Retrieval in Dense Encoders [63.28408887247742]
  We study whether training procedures can be improved to yield better generalization capabilities in the resulting models. We recommend a simple recipe for training dense encoders: Train on MSMARCO with parameter-efficient methods, such as LoRA, and opt for using in-batch negatives unless given well-constructed hard negatives.
  arXiv  Detail & Related papers  (2023-11-16T10:42:58Z)
• RA-DIT: Retrieval-Augmented Dual Instruction Tuning [90.98423540361946]
  Retrieval-augmented language models (RALMs) improve performance by accessing long-tail and up-to-date knowledge from external data stores. Existing approaches require either expensive retrieval-specific modifications to LM pre-training or use post-hoc integration of the data store that leads to suboptimal performance. We introduce Retrieval-Augmented Dual Instruction Tuning (RA-DIT), a lightweight fine-tuning methodology that provides a third option.
  arXiv  Detail & Related papers  (2023-10-02T17:16:26Z)
• Generative Data Augmentation for Commonsense Reasoning [75.26876609249197]
  G-DAUGC is a novel generative data augmentation method that aims to achieve more accurate and robust learning in the low-resource setting. G-DAUGC consistently outperforms existing data augmentation methods based on back-translation. Our analysis demonstrates that G-DAUGC produces a diverse set of fluent training examples, and that its selection and training approaches are important for performance.
  arXiv  Detail & Related papers  (2020-04-24T06:12:10Z)

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.