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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 1151–1175 of 2111 papers

TitleStatusHype
SceneRAG: Scene-level Retrieval-Augmented Generation for Video Understanding—0
ScholarCopilot: Training Large Language Models for Academic Writing with Accurate Citations—0
Scideator: Human-LLM Scientific Idea Generation Grounded in Research-Paper Facet Recombination—0
SciVer: Evaluating Foundation Models for Multimodal Scientific Claim Verification—0
SCORE: Story Coherence and Retrieval Enhancement for AI Narratives—0
SCRAG: Social Computing-Based Retrieval Augmented Generation for Community Response Forecasting in Social Media Environments—0
SEAL: Speech Embedding Alignment Learning for Speech Large Language Model with Retrieval-Augmented Generation—0
SearchRAG: Can Search Engines Be Helpful for LLM-based Medical Question Answering?—0
Search Wisely: Mitigating Sub-optimal Agentic Searches By Reducing Uncertainty—0
SecGenAI: Enhancing Security of Cloud-based Generative AI Applications within Australian Critical Technologies of National Interest—0
Secure Multifaceted-RAG for Enterprise: Hybrid Knowledge Retrieval with Security Filtering—0
Securing RAG: A Risk Assessment and Mitigation Framework—0
Generating Is Believing: Membership Inference Attacks against Retrieval-Augmented Generation—0
Self-Improving Customer Review Response Generation Based on LLMs—0
Semantic Commit: Helping Users Update Intent Specifications for AI Memory at Scale—0
Semantic Tokens in Retrieval Augmented Generation—0
SenseRAG: Constructing Environmental Knowledge Bases with Proactive Querying for LLM-Based Autonomous Driving—0
SenTest: Evaluating Robustness of Sentence Encoders—0
SEReDeEP: Hallucination Detection in Retrieval-Augmented Models via Semantic Entropy and Context-Parameter Fusion—0
SFR-RAG: Towards Contextually Faithful LLMs—0
Similarity is Not All You Need: Endowing Retrieval Augmented Generation with Multi Layered Thoughts—0
Simplifying Data Integration: SLM-Driven Systems for Unified Semantic Queries Across Heterogeneous Databases—0
SimRAG: Self-Improving Retrieval-Augmented Generation for Adapting Large Language Models to Specialized Domains—0
SIMS: Simulating Stylized Human-Scene Interactions with Retrieval-Augmented Script Generation—0
Single LLM, Multiple Roles: A Unified Retrieval-Augmented Generation Framework Using Role-Specific Token Optimization—0
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