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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 1001–1050 of 2111 papers

TitleStatusHype
MEBench: Benchmarking Large Language Models for Cross-Document Multi-Entity Question Answering—0
Trustworthy Answers, Messier Data: Bridging the Gap in Low-Resource Retrieval-Augmented Generation for Domain Expert Systems—0
Automated Code Generation and Validation for Software Components of Microcontrollers—0
Faster, Cheaper, Better: Multi-Objective Hyperparameter Optimization for LLM and RAG Systems—0
Say Less, Mean More: Leveraging Pragmatics in Retrieval-Augmented Generation—0
Detecting Knowledge Boundary of Vision Large Language Models by Sampling-Based InferenceCode0
A Hybrid Approach to Information Retrieval and Answer Generation for Regulatory TextsCode0
Graphy'our Data: Towards End-to-End Modeling, Exploring and Generating Report from Raw Data—0
Language Model Re-rankers are Steered by Lexical Similarities—0
Mitigating Bias in RAG: Controlling the EmbedderCode0
Evaluating the Effect of Retrieval Augmentation on Social Biases—0
MEMERAG: A Multilingual End-to-End Meta-Evaluation Benchmark for Retrieval Augmented GenerationCode0
Visual-RAG: Benchmarking Text-to-Image Retrieval Augmented Generation for Visual Knowledge Intensive QueriesCode0
Optimizing Retrieval-Augmented Generation of Medical Content for Spaced Repetition Learning—0
Retrieval-Augmented Visual Question Answering via Built-in Autoregressive Search Engines—0
LawPal : A Retrieval Augmented Generation Based System for Enhanced Legal Accessibility in India—0
D2S-FLOW: Automated Parameter Extraction from Datasheets for SPICE Model Generation Using Large Language Models—0
Worse than Zero-shot? A Fact-Checking Dataset for Evaluating the Robustness of RAG Against Misleading Retrievals—0
RAG-Enhanced Collaborative LLM Agents for Drug Discovery—0
An Autonomous Network Orchestration Framework Integrating Large Language Models with Continual Reinforcement Learning—0
Cross-Format Retrieval-Augmented Generation in XR with LLMs for Context-Aware Maintenance Assistance—0
Retrieval-Augmented Speech Recognition Approach for Domain Challenges—0
From Documents to Dialogue: Building KG-RAG Enhanced AI Assistants—0
Chats-Grid: An Iterative Retrieval Q&A Optimization Scheme Leveraging Large Model and Retrieval Enhancement Generation in smart grid—0
Chain-of-Rank: Enhancing Large Language Models for Domain-Specific RAG in Edge Device—0
Enhancing Domain-Specific Retrieval-Augmented Generation: Synthetic Data Generation and Evaluation using Reasoning ModelsCode0
Automated Query-Product Relevance Labeling using Large Language Models for E-commerce Search—0
On the Influence of Context Size and Model Choice in Retrieval-Augmented Generation SystemsCode0
PaperHelper: Knowledge-Based LLM QA Paper Reading Assistant—0
KITAB-Bench: A Comprehensive Multi-Domain Benchmark for Arabic OCR and Document Understanding—0
A Socratic RAG Approach to Connect Natural Language Queries on Research Topics with Knowledge Organization Systems—0
Is Relevance Propagated from Retriever to Generator in RAG?—0
FIND: Fine-grained Information Density Guided Adaptive Retrieval-Augmented Generation for Disease Diagnosis—0
Tabular Embeddings for Tables with Bi-Dimensional Hierarchical Metadata and Nesting—0
WavRAG: Audio-Integrated Retrieval Augmented Generation for Spoken Dialogue Models—0
Benchmarking Multimodal RAG through a Chart-based Document Question-Answering Generation FrameworkCode0
In-Place Updates of a Graph Index for Streaming Approximate Nearest Neighbor Search—0
RGAR: Recurrence Generation-augmented Retrieval for Factual-aware Medical Question Answering—0
HawkBench: Investigating Resilience of RAG Methods on Stratified Information-Seeking Tasks—0
Towards Context-Robust LLMs: A Gated Representation Fine-tuning Approach—0
What are Models Thinking about? Understanding Large Language Model Hallucinations "Psychology" through Model Inner State Analysis—0
Are Large Language Models In-Context Graph Learners?—0
Towards Adaptive Memory-Based Optimization for Enhanced Retrieval-Augmented Generation—0
RAG-Gym: Optimizing Reasoning and Search Agents with Process Supervision—0
DH-RAG: A Dynamic Historical Context-Powered Retrieval-Augmented Generation Method for Multi-Turn Dialogue—0
Personalized Education with Generative AI and Digital Twins: VR, RAG, and Zero-Shot Sentiment Analysis for Industry 4.0 Workforce Development—0
Infinite Retrieval: Attention Enhanced LLMs in Long-Context Processing—0
Towards an automated workflow in materials science for combining multi-modal simulative and experimental information using data mining and large language models—0
HopRAG: Multi-Hop Reasoning for Logic-Aware Retrieval-Augmented Generation—0
Oreo: A Plug-in Context Reconstructor to Enhance Retrieval-Augmented Generation—0
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