SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 676–700 of 2111 papers

TitleStatusHype
LawPal : A Retrieval Augmented Generation Based System for Enhanced Legal Accessibility in India—0
RAG-Enhanced Collaborative LLM Agents for Drug Discovery—0
An Autonomous Network Orchestration Framework Integrating Large Language Models with Continual Reinforcement Learning—0
Worse than Zero-shot? A Fact-Checking Dataset for Evaluating the Robustness of RAG Against Misleading Retrievals—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
Cross-Format Retrieval-Augmented Generation in XR with LLMs for Context-Aware Maintenance Assistance—0
Retrieval-Augmented Speech Recognition Approach for Domain Challenges—0
Is Relevance Propagated from Retriever to Generator in RAG?—0
Tabular Embeddings for Tables with Bi-Dimensional Hierarchical Metadata and Nesting—0
A Socratic RAG Approach to Connect Natural Language Queries on Research Topics with Knowledge Organization Systems—0
KITAB-Bench: A Comprehensive Multi-Domain Benchmark for Arabic OCR and Document Understanding—0
From RAG to Memory: Non-Parametric Continual Learning for Large Language ModelsCode7
Benchmarking Multimodal RAG through a Chart-based Document Question-Answering Generation FrameworkCode0
On the Influence of Context Size and Model Choice in Retrieval-Augmented Generation SystemsCode0
FIND: Fine-grained Information Density Guided Adaptive Retrieval-Augmented Generation for Disease Diagnosis—0
PaperHelper: Knowledge-Based LLM QA Paper Reading Assistant—0
WavRAG: Audio-Integrated Retrieval Augmented Generation for Spoken Dialogue Models—0
Towards Adaptive Memory-Based Optimization for Enhanced Retrieval-Augmented Generation—0
Towards Context-Robust LLMs: A Gated Representation Fine-tuning Approach—0
Personalized Education with Generative AI and Digital Twins: VR, RAG, and Zero-Shot Sentiment Analysis for Industry 4.0 Workforce Development—0
RAG-Gym: Optimizing Reasoning and Search Agents with Process Supervision—0
Show:102550
← PrevPage 28 of 85Next →

No leaderboard results yet.