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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 1226–1250 of 2111 papers

TitleStatusHype
TelecomRAG: Taming Telecom Standards with Retrieval Augmented Generation and LLMs—0
TeleRAG: Efficient Retrieval-Augmented Generation Inference with Lookahead Retrieval—0
TemPrompt: Multi-Task Prompt Learning for Temporal Relation Extraction in RAG-based Crowdsourcing Systems—0
Memory^3: Language Modeling with Explicit Memory—0
Long^2RAG: Evaluating Long-Context & Long-Form Retrieval-Augmented Generation with Key Point Recall—0
TFHE-Coder: Evaluating LLM-agentic Fully Homomorphic Encryption Code Generation—0
The Aloe Family Recipe for Open and Specialized Healthcare LLMs—0
The Chronicles of RAG: The Retriever, the Chunk and the Generator—0
The Dark Side of LLMs Agent-based Attacks for Complete Computer Takeover—0
The Design of an LLM-powered Unstructured Analytics System—0
The Distracting Effect: Understanding Irrelevant Passages in RAG—0
The Efficiency vs. Accuracy Trade-off: Optimizing RAG-Enhanced LLM Recommender Systems Using Multi-Head Early Exit—0
The Geometry of Queries: Query-Based Innovations in Retrieval-Augmented Generation—0
The Great Nugget Recall: Automating Fact Extraction and RAG Evaluation with Large Language Models—0
The Impact of Large Language Models on Task Automation in Manufacturing Services—0
The Impact of Quantization on Retrieval-Augmented Generation: An Analysis of Small LLMs—0
THELMA: Task Based Holistic Evaluation of Large Language Model Applications-RAG Question Answering—0
The RAG Paradox: A Black-Box Attack Exploiting Unintentional Vulnerabilities in Retrieval-Augmented Generation Systems—0
The Silent Saboteur: Imperceptible Adversarial Attacks against Black-Box Retrieval-Augmented Generation Systems—0
Think Before You Attribute: Improving the Performance of LLMs Attribution Systems—0
Thread: A Logic-Based Data Organization Paradigm for How-To Question Answering with Retrieval Augmented Generation—0
ThreatLens: LLM-guided Threat Modeling and Test Plan Generation for Hardware Security Verification—0
Retrieval-Augmented Feature Generation for Domain-Specific Classification—0
TimeRAF: Retrieval-Augmented Foundation model for Zero-shot Time Series Forecasting—0
TimeRAG: BOOSTING LLM Time Series Forecasting via Retrieval-Augmented Generation—0
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