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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 1126–1150 of 2111 papers

TitleStatusHype
Reward-RAG: Enhancing RAG with Reward Driven Supervision—0
ReZero: Enhancing LLM search ability by trying one-more-time—0
RGAR: Recurrence Generation-augmented Retrieval for Factual-aware Medical Question Answering—0
RichRAG: Crafting Rich Responses for Multi-faceted Queries in Retrieval-Augmented Generation—0
RISSOLE: Parameter-efficient Diffusion Models via Block-wise Generation and Retrieval-Guidance—0
Robust Action Governor for Uncertain Piecewise Affine Systems with Non-convex Constraints and Safe Reinforcement Learning—0
Robust Implementation of Retrieval-Augmented Generation on Edge-based Computing-in-Memory Architectures—0
RoseRAG: Robust Retrieval-augmented Generation with Small-scale LLMs via Margin-aware Preference Optimization—0
Rotation Invariance in Floor Plan Digitization using Zernike Moments—0
RPO: Retrieval Preference Optimization for Robust Retrieval-Augmented Generation—0
RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generation—0
RS-RAG: Bridging Remote Sensing Imagery and Comprehensive Knowledge with a Multi-Modal Dataset and Retrieval-Augmented Generation Model—0
RTLRepoCoder: Repository-Level RTL Code Completion through the Combination of Fine-Tuning and Retrieval Augmentation—0
RuAG: Learned-rule-augmented Generation for Large Language Models—0
SafeMate: A Modular RAG-Based Agent for Context-Aware Emergency Guidance—0
Safe Reinforcement Learning Using Robust Action Governor—0
SalesRLAgent: A Reinforcement Learning Approach for Real-Time Sales Conversion Prediction and Optimization—0
SARA: Selective and Adaptive Retrieval-augmented Generation with Context Compression—0
Say Less, Mean More: Leveraging Pragmatics in Retrieval-Augmented Generation—0
Scalable Defense against In-the-wild Jailbreaking Attacks with Safety Context Retrieval—0
Scalable Frame-based Construction of Sociocultural NormBases for Socially-Aware Dialogues—0
Scaling Context, Not Parameters: Training a Compact 7B Language Model for Efficient Long-Context Processing—0
Scaling Test-Time Inference with Policy-Optimized, Dynamic Retrieval-Augmented Generation via KV Caching and Decoding—0
SCALM: Detecting Bad Practices in Smart Contracts Through LLMs—0
SCAN: Semantic Document Layout Analysis for Textual and Visual Retrieval-Augmented Generation—0
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