Data Quality Challenges in Retrieval-Augmented Generation

2025-10-01 · Leopold Müller, Joshua Holstein, Sarah Bause, Gerhard Satzger, Niklas Kühl arxiv

Organizations increasingly adopt Retrieval-Augmented Generation (RAG) to enhance Large Language Models with enterprise-specific knowledge. However, current data quality (DQ) frameworks have been primarily developed for static datasets, and only inadequately address the dynamic, multi-stage nature of RAG systems. This study aims to develop DQ dimensions for this new type of AI-based systems. We conduct 16 semi-structured interviews with practitioners of leading IT service companies. Through a qualitative content analysis, we inductively derive 15 distinct DQ dimensions across the four processing stages of RAG systems: data extraction, data transformation, prompt & search, and generation. Our findings reveal that (1) new dimensions have to be added to traditional DQ frameworks to also cover RAG contexts; (2) these new dimensions are concentrated in early RAG steps, suggesting the need for front-loaded quality management strategies, and (3) DQ issues transform and propagate through the RAG pipeline, necessitating a dynamic, step-aware approach to quality management.

📄 PDF Abstract BibTeX arXiv:2510.00552

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Loops On Retrieval Augmented Generation (LoRAG)

2024-03-18 · Ayush Thakur, Rashmi Vashisth

This paper presents Loops On Retrieval Augmented Generation (LoRAG), a new framework designed to enhance the quality of retrieval-augmented text generation through the incorporation of an iterative loop mechanism. The ar…

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While Retrieval-Augmented Generation (RAG) systems enhance Large Language Models (LLMs) by incorporating external knowledge, they still face persistent challenges in retrieval inefficiency and the inability of LLMs to fi…

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2025-10-26 · Shu Zhao, Tianyi Shen, Nilesh Ahuja, Omesh Tickoo 외 arxiv

Multimodal Retrieval-Augmented Generation (MRAG) has emerged as a promising method to generate factual and up-to-date responses of Multimodal Large Language Models (MLLMs) by incorporating non-parametric knowledge from e…

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2024-05-12 · Dian Jiao, Li Cai, Jingsheng Huang, Wenqiao Zhang 외

Retrieval-Augmented Generation (RAG) methods augment the input of Large Language Models (LLMs) with relevant retrieved passages, reducing factual errors in knowledge-intensive tasks. However, contemporary RAG approaches …

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Bridge-RAG: An Abstract Bridge Tree Based Retrieval Augmented Generation Algorithm

2026-01-12 · Zihang Li, Wenjun Liu, Yikun Zong, Jiawen Tao 외 arxiv

As an important paradigm for enhancing the generation quality of Large Language Models (LLMs), retrieval-augmented generation (RAG) faces the two challenges regarding retrieval accuracy and computational efficiency. This…

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