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RAG

Retrieval-Augmented Generation (RAG) is a task that combines the strengths of both retrieval-based models and generation-based models. In this approach, a retrieval system selects relevant documents or passages from a large corpus, and a generation model, typically a neural language model, uses the retrieved information to generate a response. This method enhances the accuracy and coherence of generated text, especially in tasks requiring detailed knowledge or long context handling.

RAG is particularly useful in open-domain question answering, knowledge-grounded dialogue, and summarization tasks. The retrieval step helps the model to access and incorporate external information, making it less reliant on memorized knowledge and better suited for generating responses based on the latest or domain-specific information.

The performance of RAG systems is usually measured using metrics such as precision, recall, F1 score, BLEU score, and exact match. Some popular datasets for evaluating RAG models include Natural Questions, MS MARCO, TriviaQA, and SQuAD.

Papers

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Showing 976–1000 of 2111 papers

TitleStatusHype
RAAD-LLM: Adaptive Anomaly Detection Using LLMs and RAG Integration—0
SAGE: A Framework of Precise Retrieval for RAG—0
HoH: A Dynamic Benchmark for Evaluating the Impact of Outdated Information on Retrieval-Augmented Generation—0
SRAG: Structured Retrieval-Augmented Generation for Multi-Entity Question Answering over Wikipedia Graph—0
Towards Efficient Educational Chatbots: Benchmarking RAG Frameworks—0
Optimizing Multi-Hop Document Retrieval Through Intermediate Representations—0
ER-RAG: Enhance RAG with ER-Based Unified Modeling of Heterogeneous Data Sources—0
U-NIAH: Unified RAG and LLM Evaluation for Long Context Needle-In-A-HaystackCode0
Pseudo-Knowledge Graph: Meta-Path Guided Retrieval and In-Graph Text for RAG-Equipped LLM—0
BadJudge: Backdoor Vulnerabilities of LLM-as-a-Judge—0
Retrieval Augmented Generation for Topic Modeling in Organizational Research: An Introduction with Empirical Demonstration—0
TeleRAG: Efficient Retrieval-Augmented Generation Inference with Lookahead Retrieval—0
SuperRAG: Beyond RAG with Layout-Aware Graph Modeling—0
Fine-Grained Retrieval-Augmented Generation for Visual Question Answering—0
Ext2Gen: Alignment through Unified Extraction and Generation for Robust Retrieval-Augmented Generation—0
A Pilot Empirical Study on When and How to Use Knowledge Graphs as Retrieval Augmented Generation—0
RuCCoD: Towards Automated ICD Coding in RussianCode0
The RAG Paradox: A Black-Box Attack Exploiting Unintentional Vulnerabilities in Retrieval-Augmented Generation Systems—0
Bisecting K-Means in RAG for Enhancing Question-Answering Tasks Performance in Telecommunications—0
NANOGPT: A Query-Driven Large Language Model Retrieval-Augmented Generation System for Nanotechnology Research—0
LLM-driven Effective Knowledge Tracing by Integrating Dual-channel Difficulty—0
Mapping Trustworthiness in Large Language Models: A Bibliometric Analysis Bridging Theory to Practice—0
Trustworthy Answers, Messier Data: Bridging the Gap in Low-Resource Retrieval-Augmented Generation for Domain Expert Systems—0
MEBench: Benchmarking Large Language Models for Cross-Document Multi-Entity Question Answering—0
Leveraging Retrieval-Augmented Generation and Large Language Models to Predict SERCA-Binding Protein Fragments from Cardiac Proteomics Data—0
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