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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 601–650 of 2111 papers

TitleStatusHype
Leveraging Approximate Caching for Faster Retrieval-Augmented Generation—0
ORANSight-2.0: Foundational LLMs for O-RAN—0
Automatic Teaching Platform on Vision Language Retrieval Augmented Generation—0
FinTMMBench: Benchmarking Temporal-Aware Multi-Modal RAG in Finance—0
TS-RAG: Retrieval-Augmented Generation based Time Series Foundation Models are Stronger Zero-Shot Forecaster—0
Collapse of Dense Retrievers: Short, Early, and Literal Biases Outranking Factual Evidence—0
Beyond RAG: Task-Aware KV Cache Compression for Comprehensive Knowledge Reasoning—0
In-depth Analysis of Graph-based RAG in a Unified Framework—0
LeRAAT: LLM-Enabled Real-Time Aviation Advisory ToolCode0
LLaVE: Large Language and Vision Embedding Models with Hardness-Weighted Contrastive Learning—0
OkraLong: A Flexible Retrieval-Augmented Framework for Long-Text Query Processing—0
Optimizing open-domain question answering with graph-based retrieval augmented generation—0
Wikipedia in the Era of LLMs: Evolution and RisksCode0
PennyLang: Pioneering LLM-Based Quantum Code Generation with a Novel PennyLane-Centric Dataset—0
RAAD-LLM: Adaptive Anomaly Detection Using LLMs and RAG Integration—0
HoH: A Dynamic Benchmark for Evaluating the Impact of Outdated Information on Retrieval-Augmented Generation—0
SAGE: A Framework of Precise Retrieval for RAG—0
SRAG: Structured Retrieval-Augmented Generation for Multi-Entity Question Answering over Wikipedia Graph—0
Retrieval-Augmented Perception: High-Resolution Image Perception Meets Visual RAGCode2
SePer: Measure Retrieval Utility Through The Lens Of Semantic Perplexity ReductionCode1
ER-RAG: Enhance RAG with ER-Based Unified Modeling of Heterogeneous Data Sources—0
Optimizing Multi-Hop Document Retrieval Through Intermediate Representations—0
Towards Efficient Educational Chatbots: Benchmarking RAG Frameworks—0
GPIoT: Tailoring Small Language Models for IoT Program Synthesis and DevelopmentCode1
U-NIAH: Unified RAG and LLM Evaluation for Long Context Needle-In-A-HaystackCode0
Qilin: A Multimodal Information Retrieval Dataset with APP-level User SessionsCode2
BadJudge: Backdoor Vulnerabilities of LLM-as-a-Judge—0
Pseudo-Knowledge Graph: Meta-Path Guided Retrieval and In-Graph Text for RAG-Equipped LLM—0
SuperRAG: Beyond RAG with Layout-Aware Graph Modeling—0
Fine-Grained Retrieval-Augmented Generation for Visual Question Answering—0
LexRAG: Benchmarking Retrieval-Augmented Generation in Multi-Turn Legal Consultation ConversationCode1
Ext2Gen: Alignment through Unified Extraction and Generation for Robust Retrieval-Augmented Generation—0
DeepSolution: Boosting Complex Engineering Solution Design via Tree-based Exploration and Bi-point ThinkingCode1
Retrieval Augmented Generation for Topic Modeling in Organizational Research: An Introduction with Empirical Demonstration—0
SafeAuto: Knowledge-Enhanced Safe Autonomous Driving with Multimodal Foundation ModelsCode1
RuCCoD: Towards Automated ICD Coding in RussianCode0
A Pilot Empirical Study on When and How to Use Knowledge Graphs as Retrieval Augmented Generation—0
TeleRAG: Efficient Retrieval-Augmented Generation Inference with Lookahead Retrieval—0
The RAG Paradox: A Black-Box Attack Exploiting Unintentional Vulnerabilities in Retrieval-Augmented Generation Systems—0
EgoNormia: Benchmarking Physical Social Norm UnderstandingCode1
NANOGPT: A Query-Driven Large Language Model Retrieval-Augmented Generation System for Nanotechnology Research—0
Mapping Trustworthiness in Large Language Models: A Bibliometric Analysis Bridging Theory to Practice—0
LLM-driven Effective Knowledge Tracing by Integrating Dual-channel Difficulty—0
Bisecting K-Means in RAG for Enhancing Question-Answering Tasks Performance in Telecommunications—0
ChineseEcomQA: A Scalable E-commerce Concept Evaluation Benchmark for Large Language ModelsCode1
Long-Context Inference with Retrieval-Augmented Speculative DecodingCode1
Bridging Legal Knowledge and AI: Retrieval-Augmented Generation with Vector Stores, Knowledge Graphs, and Hierarchical Non-negative Matrix FactorizationCode1
OntologyRAG: Better and Faster Biomedical Code Mapping with Retrieval-Augmented Generation (RAG) Leveraging Ontology Knowledge Graphs and Large Language ModelsCode2
Talking like Piping and Instrumentation Diagrams (P&IDs)—0
Trustworthy Answers, Messier Data: Bridging the Gap in Low-Resource Retrieval-Augmented Generation for Domain Expert Systems—0
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