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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 1401–1425 of 2111 papers

TitleStatusHype
Multi-Document Financial Question Answering using LLMs—0
Qwen2.5-32B: Leveraging Self-Consistent Tool-Integrated Reasoning for Bengali Mathematical Olympiad Problem Solving—0
AMSnet-KG: A Netlist Dataset for LLM-based AMS Circuit Auto-Design Using Knowledge Graph RAG—0
M3DocRAG: Multi-modal Retrieval is What You Need for Multi-page Multi-document Understanding—0
Audiobox TTA-RAG: Improving Zero-Shot and Few-Shot Text-To-Audio with Retrieval-Augmented Generation—0
LLM-R: A Framework for Domain-Adaptive Maintenance Scheme Generation Combining Hierarchical Agents and RAG—0
ML-Promise: A Multilingual Dataset for Corporate Promise Verification—0
Deploying Large Language Models With Retrieval Augmented GenerationCode0
Enhancing classroom teaching with LLMs and RAG—0
Fine-Grained Guidance for Retrievers: Leveraging LLMs' Feedback in Retrieval-Augmented Generation—0
Advanced RAG Models with Graph Structures: Optimizing Complex Knowledge Reasoning and Text Generation—0
LEGO-GraphRAG: Modularizing Graph-based Retrieval-Augmented Generation for Design Space Exploration—0
RAGulator: Lightweight Out-of-Context Detectors for Grounded Text Generation—0
PersianRAG: A Retrieval-Augmented Generation System for Persian Language—0
Long Context RAG Performance of Large Language Models—0
WASHtsApp -- A RAG-powered WhatsApp Chatbot for supporting rural African clean water access, sanitation and hygiene—0
Can Language Models Enable In-Context Database?—0
RuAG: Learned-rule-augmented Generation for Large Language Models—0
Data Extraction Attacks in Retrieval-Augmented Generation via Backdoors—0
CORAG: A Cost-Constrained Retrieval Optimization System for Retrieval-Augmented Generation—0
AttackQA: Development and Adoption of a Dataset for Assisting Cybersecurity Operations using Fine-tuned and Open-Source LLMs—0
Towards Multi-Source Retrieval-Augmented Generation via Synergizing Reasoning and Preference-Driven Retrieval—0
LLM-Ref: Enhancing Reference Handling in Technical Writing with Large Language Models—0
Provenance: A Light-weight Fact-checker for Retrieval Augmented LLM Generation Output—0
JudgeRank: Leveraging Large Language Models for Reasoning-Intensive Reranking—0
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