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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 1026–1050 of 2111 papers

TitleStatusHype
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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