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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 1176–1200 of 2111 papers

TitleStatusHype
Data Extraction Attacks in Retrieval-Augmented Generation via Backdoors—0
Towards Multi-Source Retrieval-Augmented Generation via Synergizing Reasoning and Preference-Driven Retrieval—0
Provenance: A Light-weight Fact-checker for Retrieval Augmented LLM Generation Output—0
Rationale-Guided Retrieval Augmented Generation for Medical Question AnsweringCode1
CORAG: A Cost-Constrained Retrieval Optimization System for Retrieval-Augmented Generation—0
LLM-Ref: Enhancing Reference Handling in Technical Writing with Large Language Models—0
AttackQA: Development and Adoption of a Dataset for Assisting Cybersecurity Operations using Fine-tuned and Open-Source LLMs—0
JudgeRank: Leveraging Large Language Models for Reasoning-Intensive Reranking—0
Responsible Retrieval Augmented Generation for Climate Decision Making from Documents—0
LEAF: Learning and Evaluation Augmented by Fact-Checking to Improve Factualness in Large Language Models—0
Mind the Gap: A Generalized Approach for Cross-Modal Embedding Alignment—0
Eliciting Critical Reasoning in Retrieval-Augmented Language Models via Contrastive Explanations—0
Semantic Enrichment of the Quantum Cascade Laser Properties in Text- A Knowledge Graph Generation ApproachCode0
Emotional RAG: Enhancing Role-Playing Agents through Emotional RetrievalCode1
Retrieval-Augmented Generation with Estimation of Source Reliability—0
CORAL: Benchmarking Multi-turn Conversational Retrieval-Augmentation GenerationCode2
HijackRAG: Hijacking Attacks against Retrieval-Augmented Large Language Models—0
Long^2RAG: Evaluating Long-Context & Long-Form Retrieval-Augmented Generation with Key Point Recall—0
GraphAide: Advanced Graph-Assisted Query and Reasoning System—0
Beyond Text: Optimizing RAG with Multimodal Inputs for Industrial ApplicationsCode2
Calibrated Decision-Making through LLM-Assisted Retrieval—0
Combining Domain-Specific Models and LLMs for Automated Disease Phenotyping from Survey Data—0
LLMs are Biased Evaluators But Not Biased for Retrieval Augmented GenerationCode0
Simple Is Effective: The Roles of Graphs and Large Language Models in Knowledge-Graph-Based Retrieval-Augmented GenerationCode2
AutoRAG: Automated Framework for optimization of Retrieval Augmented Generation PipelineCode7
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