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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 776–800 of 2111 papers

TitleStatusHype
APE: Faster and Longer Context-Augmented Generation via Adaptive Parallel EncodingCode1
DeepThink: Aligning Language Models with Domain-Specific User Intents—0
Knowledge Graph-Guided Retrieval Augmented GenerationCode2
Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A SurveyCode2
RAG-Verus: Repository-Level Program Verification with LLMs using Retrieval Augmented Generation—0
Agentic Reasoning: Reasoning LLMs with Tools for the Deep ResearchCode0
Efficient Knowledge Feeding to Language Models: A Novel Integrated Encoder-Decoder Architecture—0
Enhancing Health Information Retrieval with RAG by Prioritizing Topical Relevance and Factual Accuracy—0
LLMs to Support a Domain Specific Knowledge Assistant—0
MRAMG-Bench: A Comprehensive Benchmark for Advancing Multimodal Retrieval-Augmented Multimodal GenerationCode1
MedRAG: Enhancing Retrieval-augmented Generation with Knowledge Graph-Elicited Reasoning for Healthcare CopilotCode3
Enhancing Online Learning Efficiency Through Heterogeneous Resource Integration with a Multi-Agent RAG System—0
Experiments with Large Language Models on Retrieval-Augmented Generation for Closed-Source Simulation Software—0
Cache-Craft: Managing Chunk-Caches for Efficient Retrieval-Augmented Generation—0
MARAGE: Transferable Multi-Model Adversarial Attack for Retrieval-Augmented Generation Data Extraction—0
Spatial-RAG: Spatial Retrieval Augmented Generation for Real-World Geospatial Reasoning Questions—0
SCALM: Detecting Bad Practices in Smart Contracts Through LLMs—0
Open Foundation Models in Healthcare: Challenges, Paradoxes, and Opportunities with GenAI Driven Personalized Prescription—0
Conversation AI Dialog for Medicare powered by Finetuning and Retrieval Augmented Generation—0
Rankify: A Comprehensive Python Toolkit for Retrieval, Re-Ranking, and Retrieval-Augmented GenerationCode4
Personalization Toolkit: Training Free Personalization of Large Vision Language Models—0
OverThink: Slowdown Attacks on Reasoning LLMsCode1
LLMSecConfig: An LLM-Based Approach for Fixing Software Container Misconfigurations—0
Knowledge Synthesis of Photosynthesis Research Using a Large Language Model—0
VideoRAG: Retrieval-Augmented Generation with Extreme Long-Context VideosCode7
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