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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 201–250 of 2111 papers

TitleStatusHype
Reranking with Compressed Document Representation—0
Silent Leaks: Implicit Knowledge Extraction Attack on RAG Systems through Benign QueriesCode1
HDLxGraph: Bridging Large Language Models and HDL Repositories via HDL Graph DatabasesCode0
Adaptive Plan-Execute Framework for Smart Contract Security Auditing—0
Scalable Defense against In-the-wild Jailbreaking Attacks with Safety Context Retrieval—0
Single LLM, Multiple Roles: A Unified Retrieval-Augmented Generation Framework Using Role-Specific Token Optimization—0
RAG/LLM Augmented Switching Driven Polymorphic Metaheuristic Framework—0
Multimodal RAG-driven Anomaly Detection and Classification in Laser Powder Bed Fusion using Large Language Models—0
SCAN: Semantic Document Layout Analysis for Textual and Visual Retrieval-Augmented Generation—0
Beyond Chains: Bridging Large Language Models and Knowledge Bases in Complex Question Answering—0
MultiHal: Multilingual Dataset for Knowledge-Graph Grounded Evaluation of LLM HallucinationsCode0
s3: You Don't Need That Much Data to Train a Search Agent via RLCode4
Benchmarking the Myopic Trap: Positional Bias in Information RetrievalCode5
RAVENEA: A Benchmark for Multimodal Retrieval-Augmented Visual Culture UnderstandingCode0
Process vs. Outcome Reward: Which is Better for Agentic RAG Reinforcement LearningCode1
Automatic Dataset Generation for Knowledge Intensive Question Answering Tasks—0
Beyond Text: Unveiling Privacy Vulnerabilities in Multi-modal Retrieval-Augmented Generation—0
Divide by Question, Conquer by Agent: SPLIT-RAG with Question-Driven Graph Partitioning—0
Know Or Not: a library for evaluating out-of-knowledge base robustnessCode1
AMAQA: A Metadata-based QA Dataset for RAG Systems—0
Re-identification of De-identified Documents with Autoregressive Infilling—0
Think Before You Attribute: Improving the Performance of LLMs Attribution Systems—0
Towards A Generalist Code Embedding Model Based On Massive Data SynthesisCode0
Evaluating the Performance of RAG Methods for Conversational AI in the Airport Domain—0
Effective and Transparent RAG: Adaptive-Reward Reinforcement Learning for Decision TraceabilityCode1
RAR: Setting Knowledge Tripwires for Retrieval Augmented Rejection—0
Optimizing Retrieval Augmented Generation for Object Constraint Language—0
Accelerating Adaptive Retrieval Augmented Generation via Instruction-Driven Representation Reduction of Retrieval Overlaps—0
Know3-RAG: A Knowledge-aware RAG Framework with Adaptive Retrieval, Generation, and FilteringCode0
RAGXplain: From Explainable Evaluation to Actionable Guidance of RAG Pipelines—0
PoisonArena: Uncovering Competing Poisoning Attacks in Retrieval-Augmented GenerationCode0
Neuro-Symbolic Query CompilerCode1
Telco-oRAG: Optimizing Retrieval-augmented Generation for Telecom Queries via Hybrid Retrieval and Neural Routing—0
ELITE: Embedding-Less retrieval with Iterative Text ExplorationCode1
Let's have a chat with the EU AI Act—0
Unveiling Knowledge Utilization Mechanisms in LLM-based Retrieval-Augmented Generation—0
EcoSafeRAG: Efficient Security through Context Analysis in Retrieval-Augmented Generation—0
THELMA: Task Based Holistic Evaluation of Large Language Model Applications-RAG Question Answering—0
MIRACL-VISION: A Large, multilingual, visual document retrieval benchmark—0
mmRAG: A Modular Benchmark for Retrieval-Augmented Generation over Text, Tables, and Knowledge GraphsCode1
RAGSynth: Synthetic Data for Robust and Faithful RAG Component OptimizationCode1
SubGCache: Accelerating Graph-based RAG with Subgraph-level KV Cache—0
A Dataset for Spatiotemporal-Sensitive POI Question AnsweringCode0
Enhancing Low-Resource Minority Language Translation with LLMs and Retrieval-Augmented Generation for Cultural Nuances—0
Finetune-RAG: Fine-Tuning Language Models to Resist Hallucination in Retrieval-Augmented GenerationCode1
DO-RAG: A Domain-Specific QA Framework Using Knowledge Graph-Enhanced Retrieval-Augmented GenerationCode0
One Shot Dominance: Knowledge Poisoning Attack on Retrieval-Augmented Generation Systems—0
Personalizing Large Language Models using Retrieval Augmented Generation and Knowledge GraphCode0
AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges—0
GE-Chat: A Graph Enhanced RAG Framework for Evidential Response Generation of LLMs—0
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