mlsanityData to 14 Oct 2026

Optimizing Retrieval-Augmented Generation with Elasticsearch for Enhanced Question-Answering Systems

arXiv 2410.14167 ↗First posted 18 Oct 2024cs.IRcited by 5

On the map: Retrieval-augmented generation → RAG robustness and adaptive retrieval

Abstract (arXiv)

This study aims to improve the accuracy and quality of large-scale language models (LLMs) in answering questions by integrating Elasticsearch into the Retrieval Augmented Generation (RAG) framework. The experiment uses the Stanford Question Answering Dataset (SQuAD) version 2.0 as the test dataset and compares the performance of different retrieval methods, including traditional methods based on keyword matching or semantic similarity calculation, BM25-RAG and TF-IDF- RAG, and the newly proposed ES-RAG scheme. The results show that ES-RAG not only has obvious advantages in retrieval efficiency but also performs well in key indicators such as accuracy, which is 0.51 percentage points higher than TF-IDF-RAG. In addition, Elasticsearch's powerful search capabilities and rich configuration options enable the entire question-answering system to better handle complex queries and provide more flexible and efficient responses based on the diverse needs of users. Future research directions can further explore how to optimize the interaction mechanism between Elasticsearch and LLM, such as introducing higher-level semantic understanding and context-awareness capabilities, to achieve a more intelligent and humanized question-answering experience.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation. Builds on 9 of its 23 references that are in the map. First posted on arXiv 18 Oct 2024.

Builds on

9 of 23 references.

References · 9

  1. 2024Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Miningcited by 47
  2. 2024Open-RAG: Enhanced Retrieval-Augmented Reasoning with Open-Source Large Language Modelscited by 25
  3. 2024Dynamic Fraud Detection: Integrating Reinforcement Learning into Graph Neural Networkscited by 19
  4. 2024Research on Adverse Drug Reaction Prediction Model Combining Knowledge Graph Embedding and Deep Learningcited by 17
  5. 2024Wasserstein Distance-Weighted Adversarial Network for Cross-Domain Credit Risk Assessmentcited by 11
  6. 2024Algorithm Research of ELMo Word Embedding and Deep Learning Multimodal Transformer in Image Descriptioncited by 10
  7. 2024Unveiling the Potential of Graph Neural Networks in SME Credit Risk Assessmentcited by 9
  8. 2024Convolutional Neural Networks for Predictive Modeling of Lung Diseasecited by 7
  9. 2024Graph Neural Network Framework for Sentiment Analysis Using Syntactic Featurecited by 6

Built on by

Cited by year

Citing papers per year: 2024: 5.

5 papers.

  1. 2024Emotion-Aware Interaction Design in Intelligent User Interface Using Multi-Modal Deep Learningcited by 13
  2. 2024Self-Supervised Learning in Deep Networks: A Pathway to Robust Few-Shot Classificationcited by 11
  3. 2024Reinforcement Learning for Adaptive Resource Scheduling in Complex System Environmentscited by 7
  4. 2024Advanced Risk Prediction and Stability Assessment of Banks Using Time Series Transformer Modelscited by 6
  5. 2024Leveraging Generative Adversarial Networks for Addressing Data Imbalance in Financial Market Supervisioncited by 6

Similar, not cited

Close in wording, no citation either way

  1. 2025A Systematic Review of Key Retrieval-Augmented Generation (RAG) Systems: Progress, Gaps, and Future Directions
  2. 2024A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions
  3. 2025QuIM-RAG: Advancing Retrieval-Augmented Generation with Inverted Question Matching for Enhanced QA Performance
  4. 2024Improving Retrieval for RAG based Question Answering Models on Financial Documents
  5. 2024Retrieval-Augmented Generation for Domain-Specific Question Answering: A Case Study on Pittsburgh and CMU
  6. 2025Enhancing Retrieval-Augmented Generation: A Study of Best Practices
  7. 2025A Survey on Knowledge-Oriented Retrieval-Augmented Generation
  8. 2024Evaluation of RAG Metrics for Question Answering in the Telecom Domain