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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 251–300 of 2111 papers

TitleStatusHype
NUDGE: Lightweight Non-Parametric Fine-Tuning of Embeddings for RetrievalCode1
Neural Exec: Learning (and Learning from) Execution Triggers for Prompt Injection AttacksCode1
Neuro-Symbolic Query CompilerCode1
PA-RAG: RAG Alignment via Multi-Perspective Preference OptimizationCode1
AT-RAG: An Adaptive RAG Model Enhancing Query Efficiency with Topic Filtering and Iterative ReasoningCode1
AtomR: Atomic Operator-Empowered Large Language Models for Heterogeneous Knowledge ReasoningCode1
NeuSym-RAG: Hybrid Neural Symbolic Retrieval with Multiview Structuring for PDF Question AnsweringCode1
One Token Can Help! Learning Scalable and Pluggable Virtual Tokens for Retrieval-Augmented Large Language ModelsCode1
MRAMG-Bench: A Comprehensive Benchmark for Advancing Multimodal Retrieval-Augmented Multimodal GenerationCode1
BRIEF: Bridging Retrieval and Inference for Multi-hop Reasoning via CompressionCode1
MRD-RAG: Enhancing Medical Diagnosis with Multi-Round Retrieval-Augmented GenerationCode1
Multi-Meta-RAG: Improving RAG for Multi-Hop Queries using Database Filtering with LLM-Extracted MetadataCode1
MM-PoisonRAG: Disrupting Multimodal RAG with Local and Global Poisoning AttacksCode1
MIRAGE-Bench: Automatic Multilingual Benchmark Arena for Retrieval-Augmented Generation SystemsCode1
Model Internals-based Answer Attribution for Trustworthy Retrieval-Augmented GenerationCode1
Multi-modal Retrieval Augmented Multi-modal Generation: A Benchmark, Evaluate Metrics and Strong BaselinesCode1
Merging-Diverging Hybrid Transformer Networks for Survival Prediction in Head and Neck CancerCode1
MemLLM: Finetuning LLMs to Use An Explicit Read-Write MemoryCode1
MedPix 2.0: A Comprehensive Multimodal Biomedical Data set for Advanced AI ApplicationsCode1
Med-R^2: Crafting Trustworthy LLM Physicians via Retrieval and Reasoning of Evidence-Based MedicineCode1
GPIoT: Tailoring Small Language Models for IoT Program Synthesis and DevelopmentCode1
MetaGen Blended RAG: Higher Accuracy for Domain-Specific Q&A Without Fine-TuningCode1
MBA-RAG: a Bandit Approach for Adaptive Retrieval-Augmented Generation through Question ComplexityCode1
MacRAG: Compress, Slice, and Scale-up for Multi-Scale Adaptive Context RAGCode1
LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff TableCode1
AssistRAG: Boosting the Potential of Large Language Models with an Intelligent Information AssistantCode1
Long-Context Inference with Retrieval-Augmented Speculative DecodingCode1
Long Context vs. RAG for LLMs: An Evaluation and RevisitsCode1
LLM-Lasso: A Robust Framework for Domain-Informed Feature Selection and RegularizationCode1
LLM-Empowered Embodied Agent for Memory-Augmented Task Planning in Household RoboticsCode1
LLMs Know What They Need: Leveraging a Missing Information Guided Framework to Empower Retrieval-Augmented GenerationCode1
LexRAG: Benchmarking Retrieval-Augmented Generation in Multi-Turn Legal Consultation ConversationCode1
Leveraging Fine-Tuned Retrieval-Augmented Generation with Long-Context Support: For 3GPP StandardsCode1
LexDrafter: Terminology Drafting for Legislative Documents using Retrieval Augmented GenerationCode1
Logic-RAG: Augmenting Large Multimodal Models with Visual-Spatial Knowledge for Road Scene UnderstandingCode1
L-CiteEval: Do Long-Context Models Truly Leverage Context for Responding?Code1
Less is More: Making Smaller Language Models Competent Subgraph Retrievers for Multi-hop KGQACode1
Know Or Not: a library for evaluating out-of-knowledge base robustnessCode1
KnowTrace: Bootstrapping Iterative Retrieval-Augmented Generation with Structured Knowledge TracingCode1
Bridging Legal Knowledge and AI: Retrieval-Augmented Generation with Vector Stores, Knowledge Graphs, and Hierarchical Non-negative Matrix FactorizationCode1
SafeAuto: Knowledge-Enhanced Safe Autonomous Driving with Multimodal Foundation ModelsCode1
Knowledge graph enhanced retrieval-augmented generation for failure mode and effects analysisCode1
LaB-RAG: Label Boosted Retrieval Augmented Generation for Radiology Report GenerationCode1
AgentAda: Skill-Adaptive Data Analytics for Tailored Insight DiscoveryCode1
KG-HTC: Integrating Knowledge Graphs into LLMs for Effective Zero-shot Hierarchical Text ClassificationCode1
JuDGE: Benchmarking Judgment Document Generation for Chinese Legal SystemCode1
JORA: JAX Tensor-Parallel LoRA Library for Retrieval Augmented Fine-TuningCode1
KG-Retriever: Efficient Knowledge Indexing for Retrieval-Augmented Large Language ModelsCode1
InteractiveSurvey: An LLM-based Personalized and Interactive Survey Paper Generation SystemCode1
Jasper and Stella: distillation of SOTA embedding modelsCode1
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