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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 651–700 of 2111 papers

TitleStatusHype
Towards A Generalist Code Embedding Model Based On Massive Data Synthesis—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
Optimizing Retrieval Augmented Generation for Object Constraint Language—0
Re-identification of De-identified Documents with Autoregressive Infilling—0
Evaluating the Performance of RAG Methods for Conversational AI in the Airport Domain—0
AMAQA: A Metadata-based QA Dataset for RAG Systems—0
RAGXplain: From Explainable Evaluation to Actionable Guidance of RAG Pipelines—0
PoisonArena: Uncovering Competing Poisoning Attacks in Retrieval-Augmented GenerationCode0
Unveiling Knowledge Utilization Mechanisms in LLM-based Retrieval-Augmented Generation—0
Telco-oRAG: Optimizing Retrieval-augmented Generation for Telecom Queries via Hybrid Retrieval and Neural Routing—0
Let's have a chat with the EU AI Act—0
THELMA: Task Based Holistic Evaluation of Large Language Model Applications-RAG Question Answering—0
EcoSafeRAG: Efficient Security through Context Analysis in Retrieval-Augmented Generation—0
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
MIRACL-VISION: A Large, multilingual, visual document retrieval benchmark—0
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
CL-RAG: Bridging the Gap in Retrieval-Augmented Generation with Curriculum Learning—0
CAFE: Retrieval Head-based Coarse-to-Fine Information Seeking to Enhance Multi-Document QA Capability—0
One Shot Dominance: Knowledge Poisoning Attack on Retrieval-Augmented Generation Systems—0
Leveraging Graph Retrieval-Augmented Generation to Support Learners' Understanding of Knowledge Concepts in MOOCs—0
Personalizing Large Language Models using Retrieval Augmented Generation and Knowledge GraphCode0
DO-RAG: A Domain-Specific QA Framework Using Knowledge Graph-Enhanced Retrieval-Augmented GenerationCode0
XRAG: Cross-lingual Retrieval-Augmented Generation—0
CXMArena: Unified Dataset to benchmark performance in realistic CXM ScenariosCode0
The Impact of Large Language Models on Task Automation in Manufacturing Services—0
A Multimodal Multi-Agent Framework for Radiology Report Generation—0
Do Large Language Models Know Conflict? Investigating Parametric vs. Non-Parametric Knowledge of LLMs for Conflict Forecasting—0
Towards Automated Situation Awareness: A RAG-Based Framework for Peacebuilding Reports—0
Hakim: Farsi Text Embedding Model—0
Securing RAG: A Risk Assessment and Mitigation Framework—0
Scaling Context, Not Parameters: Training a Compact 7B Language Model for Efficient Long-Context Processing—0
IterKey: Iterative Keyword Generation with LLMs for Enhanced Retrieval Augmented Generation—0
WixQA: A Multi-Dataset Benchmark for Enterprise Retrieval-Augmented Generation—0
Enhancing Thyroid Cytology Diagnosis with RAG-Optimized LLMs and Pa-thology Foundation Models—0
Optimizing Retrieval-Augmented Generation: Analysis of Hyperparameter Impact on Performance and Efficiency—0
Enhancing Cache-Augmented Generation (CAG) with Adaptive Contextual Compression for Scalable Knowledge Integration—0
Improving the Reliability of LLMs: Combining CoT, RAG, Self-Consistency, and Self-Verification—0
Aitomia: Your Intelligent Assistant for AI-Driven Atomistic and Quantum Chemical Simulations—0
Towards Requirements Engineering for RAG Systems—0
MedEIR: A Specialized Medical Embedding Model for Enhanced Information Retrieval—0
GRADA: Graph-based Reranker against Adversarial Documents AttackCode0
SEReDeEP: Hallucination Detection in Retrieval-Augmented Models via Semantic Entropy and Context-Parameter Fusion—0
Why Uncertainty Estimation Methods Fall Short in RAG: An Axiomatic Analysis—0
KAQG: A Knowledge-Graph-Enhanced RAG for Difficulty-Controlled Question Generation—0
Benchmarking Retrieval-Augmented Generation for Chemistry—0
ThreatLens: LLM-guided Threat Modeling and Test Plan Generation for Hardware Security Verification—0
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