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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 951–975 of 2111 papers

TitleStatusHype
Pirates of the RAG: Adaptively Attacking LLMs to Leak Knowledge Bases—0
EvoPat: A Multi-LLM-based Patents Summarization and Analysis Agent—0
Molly: Making Large Language Model Agents Solve Python Problem More Logically—0
Improving Factuality with Explicit Working Memory—0
GeAR: Graph-enhanced Agent for Retrieval-augmented Generation—0
Correctness is not Faithfulness in RAG Attributions—0
Contrato360 2.0: A Document and Database-Driven Question-Answer System using Large Language Models and Agents—0
Dynamic Multi-Agent Orchestration and Retrieval for Multi-Source Question-Answer Systems using Large Language Models—0
A Survey of Query Optimization in Large Language Models—0
RAGONITE: Iterative Retrieval on Induced Databases and Verbalized RDF for Conversational QA over KGs with RAG—0
Efficient fine-tuning methodology of text embedding models for information retrieval: contrastive learning penalty (clp)Code1
The HalluRAG Dataset: Detecting Closed-Domain Hallucinations in RAG Applications Using an LLM's Internal StatesCode0
A Reality Check on Context Utilisation for Retrieval-Augmented GenerationCode0
LLM Agent for Fire Dynamics Simulations—0
InfoTech Assistant : A Multimodal Conversational Agent for InfoTechnology Web Portal Queries—0
Large Language Model Can Be a Foundation for Hidden Rationale-Based RetrievalCode0
AlzheimerRAG: Multimodal Retrieval Augmented Generation for PubMed articles—0
Towards More Robust Retrieval-Augmented Generation: Evaluating RAG Under Adversarial Poisoning AttacksCode0
Speech Retrieval-Augmented Generation without Automatic Speech Recognition—0
Formal Language Knowledge Corpus for Retrieval Augmented Generation—0
TimeRAG: BOOSTING LLM Time Series Forecasting via Retrieval-Augmented Generation—0
HybGRAG: Hybrid Retrieval-Augmented Generation on Textual and Relational Knowledge Bases—0
Don't Do RAG: When Cache-Augmented Generation is All You Need for Knowledge TasksCode5
Towards Interpretable Radiology Report Generation via Concept Bottlenecks using a Multi-Agentic RAGCode1
XRAG: eXamining the Core -- Benchmarking Foundational Components in Advanced Retrieval-Augmented GenerationCode2
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