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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 1576–1600 of 2111 papers

TitleStatusHype
Retrieval Augmented Generation Integrated Large Language Models in Smart Contract Vulnerability Detection—0
Differential Privacy of Cross-Attention with Provable Guarantee—0
Adversarial Databases Improve Success in Retrieval-based Large Language Models—0
ChatQA 2: Bridging the Gap to Proprietary LLMs in Long Context and RAG Capabilities—0
RAG-QA Arena: Evaluating Domain Robustness for Long-form Retrieval Augmented Question AnsweringCode2
Unipa-GPT: Large Language Models for university-oriented QA in ItalianCode0
PRAGyan -- Connecting the Dots in Tweets—0
Visual Haystacks: A Vision-Centric Needle-In-A-Haystack BenchmarkCode1
Retrieve, Summarize, Plan: Advancing Multi-hop Question Answering with an Iterative Approach—0
Retrieval-Augmented Generation for Natural Language Processing: A Survey—0
Black-Box Opinion Manipulation Attacks to Retrieval-Augmented Generation of Large Language Models—0
Can Open-Source LLMs Compete with Commercial Models? Exploring the Few-Shot Performance of Current GPT Models in Biomedical TasksCode0
Explainable Biomedical Hypothesis Generation via Retrieval Augmented Generation enabled Large Language Models—0
Optimizing Query Generation for Enhanced Document Retrieval in RAG—0
EchoSight: Advancing Visual-Language Models with Wiki Knowledge—0
Evaluating Search Engines and Large Language Models for Answering Health QuestionsCode0
AgentPoison: Red-teaming LLM Agents via Poisoning Memory or Knowledge BasesCode3
Mindful-RAG: A Study of Points of Failure in Retrieval Augmented Generation—0
A Comprehensive Evaluation of Large Language Models on Temporal Event Forecasting—0
Scientific QA System with Verifiable AnswersCode2
Better RAG using Relevant Information GainCode0
Knowledge and Aptitude Augmented Generation: Adaptive Multi-Turn Interaction in LLM SystemsCode0
Evaluation of RAG Metrics for Question Answering in the Telecom Domain—0
MixGR: Enhancing Retriever Generalization for Scientific Domain through Complementary GranularityCode1
Communication- and Computation-Efficient Distributed Submodular Optimization in Robot Mesh NetworksCode0
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