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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 626–650 of 2111 papers

TitleStatusHype
Align-GRAG: Reasoning-Guided Dual Alignment for Graph Retrieval-Augmented Generation—0
BAGELS: Benchmarking the Automated Generation and Extraction of Limitations from Scholarly Text—0
VoxRAG: A Step Toward Transcription-Free RAG Systems in Spoken Question Answering—0
Walk&Retrieve: Simple Yet Effective Zero-shot Retrieval-Augmented Generation via Knowledge Graph WalksCode0
Reranking with Compressed Document Representation—0
HDLxGraph: Bridging Large Language Models and HDL Repositories via HDL Graph DatabasesCode0
InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation—0
After Retrieval, Before Generation: Enhancing the Trustworthiness of Large Language Models in RAG—0
Single LLM, Multiple Roles: A Unified Retrieval-Augmented Generation Framework Using Role-Specific Token Optimization—0
BR-TaxQA-R: A Dataset for Question Answering with References for Brazilian Personal Income Tax Law, including case law—0
Ranking Free RAG: Replacing Re-ranking with Selection in RAG for Sensitive Domains—0
Do RAG Systems Suffer From Positional Bias?—0
Adaptive Plan-Execute Framework for Smart Contract Security Auditing—0
Scalable Defense against In-the-wild Jailbreaking Attacks with Safety Context Retrieval—0
Multimodal RAG-driven Anomaly Detection and Classification in Laser Powder Bed Fusion using Large Language Models—0
SCAN: Semantic Document Layout Analysis for Textual and Visual Retrieval-Augmented Generation—0
RAVENEA: A Benchmark for Multimodal Retrieval-Augmented Visual Culture UnderstandingCode0
MultiHal: Multilingual Dataset for Knowledge-Graph Grounded Evaluation of LLM HallucinationsCode0
RAG/LLM Augmented Switching Driven Polymorphic Metaheuristic Framework—0
Beyond Text: Unveiling Privacy Vulnerabilities in Multi-modal Retrieval-Augmented Generation—0
Beyond Chains: Bridging Large Language Models and Knowledge Bases in Complex Question Answering—0
Divide by Question, Conquer by Agent: SPLIT-RAG with Question-Driven Graph Partitioning—0
Automatic Dataset Generation for Knowledge Intensive Question Answering Tasks—0
Optimizing Retrieval Augmented Generation for Object Constraint Language—0
Know3-RAG: A Knowledge-aware RAG Framework with Adaptive Retrieval, Generation, and FilteringCode0
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