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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 1326–1350 of 2111 papers

TitleStatusHype
LLaVA Needs More Knowledge: Retrieval Augmented Natural Language Generation with Knowledge Graph for Explaining Thoracic PathologiesCode0
Driving with Regulation: Interpretable Decision-Making for Autonomous Vehicles with Retrieval-Augmented Reasoning via LLM—0
Reasoning-Enhanced Healthcare Predictions with Knowledge Graph Community RetrievalCode2
Inference Scaling for Long-Context Retrieval Augmented Generation—0
MindScope: Exploring cognitive biases in large language models through Multi-Agent SystemsCode0
Assessing the Performance of Human-Capable LLMs -- Are LLMs Coming for Your Job?—0
Consistent Autoformalization for Constructing Mathematical LibrariesCode0
Metadata-based Data Exploration with Retrieval-Augmented Generation for Large Language Models—0
ORAssistant: A Custom RAG-based Conversational Assistant for OpenROAD—0
Auto-GDA: Automatic Domain Adaptation for Efficient Grounding Verification in Retrieval Augmented Generation—0
SwiftKV: Fast Prefill-Optimized Inference with Knowledge-Preserving Model TransformationCode3
Ward: Provable RAG Dataset Inference via LLM Watermarks—0
Scalable Frame-based Construction of Sociocultural NormBases for Socially-Aware Dialogues—0
A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions—0
Reward-RAG: Enhancing RAG with Reward Driven Supervision—0
L-CiteEval: Do Long-Context Models Truly Leverage Context for Responding?Code1
Adversarial Decoding: Generating Readable Documents for Adversarial ObjectivesCode1
Intrinsic Evaluation of RAG Systems for Deep-Logic Questions—0
UncertaintyRAG: Span-Level Uncertainty Enhanced Long-Context Modeling for Retrieval-Augmented Generation—0
HELMET: How to Evaluate Long-Context Language Models Effectively and ThoroughlyCode3
Domain-Specific Retrieval-Augmented Generation Using Vector Stores, Knowledge Graphs, and Tensor Factorization—0
How Much Can RAG Help the Reasoning of LLM?—0
ColaCare: Enhancing Electronic Health Record Modeling through Large Language Model-Driven Multi-Agent CollaborationCode1
Segment as You Wish -- Free-Form Language-Based Segmentation for Medical ImagesCode0
Enhancing Retrieval in QA Systems with Derived Feature AssociationCode0
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