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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 1401–1450 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
LEAF: Learning and Evaluation Augmented by Fact-Checking to Improve Factualness in Large Language Models—0
Responsible Retrieval Augmented Generation for Climate Decision Making from Documents—0
Retrieval-Augmented Generation with Estimation of Source Reliability—0
Eliciting Critical Reasoning in Retrieval-Augmented Language Models via Contrastive Explanations—0
Long^2RAG: Evaluating Long-Context & Long-Form Retrieval-Augmented Generation with Key Point Recall—0
Semantic Enrichment of the Quantum Cascade Laser Properties in Text- A Knowledge Graph Generation ApproachCode0
Mind the Gap: A Generalized Approach for Cross-Modal Embedding Alignment—0
HijackRAG: Hijacking Attacks against Retrieval-Augmented Large Language Models—0
GraphAide: Advanced Graph-Assisted Query and Reasoning System—0
CRAT: A Multi-Agent Framework for Causality-Enhanced Reflective and Retrieval-Augmented Translation with Large Language Models—0
PlanRAG: Planning-guided Retrieval Augmented Generation—0
LLMs are Biased Evaluators But Not Biased for Retrieval Augmented GenerationCode0
Calibrated Decision-Making through LLM-Assisted Retrieval—0
Geo-FuB: A Method for Constructing an Operator-Function Knowledge Base for Geospatial Code Generation Tasks Using Large Language ModelsCode0
Combining Domain-Specific Models and LLMs for Automated Disease Phenotyping from Survey Data—0
R^3AG: First Workshop on Refined and Reliable Retrieval Augmented Generation—0
LLM Robustness Against Misinformation in Biomedical Question AnsweringCode0
Mask-based Membership Inference Attacks for Retrieval-Augmented Generation—0
EfficientEQA: An Efficient Approach for Open Vocabulary Embodied Question Answering—0
ChunkRAG: Novel LLM-Chunk Filtering Method for RAG Systems—0
FISHNET: Financial Intelligence from Sub-querying, Harmonizing, Neural-Conditioning, Expert Swarms, and Task Planning—0
An LLM Agent for Automatic Geospatial Data Analysis—0
Bielik 7B v0.1: A Polish Language Model -- Development, Insights, and Evaluation—0
Aggregated Knowledge Model: Enhancing Domain-Specific QA with Fine-Tuned and Retrieval-Augmented Generation Models—0
SimRAG: Self-Improving Retrieval-Augmented Generation for Adapting Large Language Models to Specialized Domains—0
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