OCR 阻礙了 RAG：評估 OCR 對檢索增強生成的串聯影響。
OCR Hinders RAG: Evaluating the Cascading Impact of OCR on Retrieval-Augmented Generation
摘要
Summary
AI-Generated Summary
Paper Overview
The paper introduces OHRBench, a benchmark for evaluating OCR's impact on Retrieval-Augmented Generation (RAG) systems. It identifies Semantic and Formatting Noise types, perturbs data accordingly, and evaluates OCR solutions' performance. The study highlights the vulnerability of RAG systems to OCR noise and discusses the potential of Vision-Language Models (VLMs) in RAG applications.
Core Contribution
- Introduction of OHRBench benchmark for assessing OCR impact on RAG systems.
- Identification and perturbation of Semantic and Formatting Noise types.
- Comprehensive evaluation of OCR solutions and their impact on RAG performance.
- Analysis of the vulnerability of RAG systems to OCR noise.
- Discussion on the potential of Vision-Language Models in RAG applications.
Research Context
The paper addresses the gap in existing benchmarks by focusing on OCR's cascading impact on RAG systems. It explores the effects of Semantic and Formatting Noise on RAG components, evaluates OCR solutions comprehensively, and discusses the potential of VLMs in enhancing RAG performance.
Keywords
OCR, Retrieval-Augmented Generation (RAG), OHRBench, Semantic Noise, Formatting Noise, Vision-Language Models (VLMs), Benchmarking, Multimodal Elements, Knowledge Bases
Background
The research background involves the need to evaluate OCR's impact on RAG systems. The study aims to address the lack of benchmarks focusing on OCR noise effects on RAG components. By perturbing data with Semantic and Formatting Noise, the paper aims to assess OCR solutions' competency for constructing high-quality knowledge bases for RAG systems.
Research Gap
Existing literature lacks benchmarks that specifically evaluate OCR's impact on RAG systems.
Technical Challenges
Challenges include identifying and perturbing Semantic and Formatting Noise types in OCR data.
Prior Approaches
Existing solutions have not comprehensively evaluated OCR's impact on constructing knowledge bases for RAG systems.
Methodology
The research methodology involves perturbing data with Semantic and Formatting Noise to evaluate OCR solutions' performance and their impact on RAG systems.
Theoretical Foundation
The study is based on assessing OCR noise effects on RAG components and systems.
Technical Architecture
Data perturbation involves introducing Semantic and Formatting Noise to mimic OCR errors.
Implementation Details
Specific algorithms and tools are used to generate perturbed data and evaluate OCR solutions.
Innovation Points
The study innovates by introducing OHRBench, identifying OCR noise types, evaluating OCR solutions comprehensively, and analyzing OCR noise's impact on RAG systems.
Experimental Validation
The experimental validation assesses OCR solutions' performance on RAG systems using perturbed data with Semantic and Formatting Noise.
Setup
Data perturbation involves introducing varying levels of Semantic and Formatting Noise.
Metrics
Evaluation metrics include LCS@1, LCS@5, EM, F1, EM@1, and F1@1 for assessing OCR solutions' performance.
Results
Results show the impact of Semantic and Formatting Noise on RAG systems and OCR solutions' competency.
Comparative Analysis
The study compares OCR solutions' performance across different domains and evaluates the impact of noise on RAG components.
Impact and Implications
The research findings have implications for improving OCR solutions for RAG systems and highlight the potential of Vision-Language Models in enhancing RAG performance.
Key Findings
The study reveals the vulnerability of RAG systems to OCR noise and the need for improved OCR solutions.
Limitations
Current OCR solutions exhibit performance loss in RAG applications, indicating the need for advancements.
Future Directions
Future research can focus on developing OCR solutions resilient to Semantic and Formatting Noise.
Practical Significance
The study's findings can lead to the development of more robust OCR systems for RAG applications.