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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 1376–1400 of 2111 papers

TitleStatusHype
A Novel Approach to Eliminating Hallucinations in Large Language Model-Assisted Causal Discovery—0
Empowering Meta-Analysis: Leveraging Large Language Models for Scientific SynthesisCode0
Harnessing multiple LLMs for Information Retrieval: A case study on Deep Learning methodologies in Biodiversity publicationsCode0
Adopting RAG for LLM-Aided Future Vehicle Design—0
Comprehensive and Practical Evaluation of Retrieval-Augmented Generation Systems for Medical Question Answering—0
Building Trustworthy AI: Transparent AI Systems via Large Language Models, Ontologies, and Logical Reasoning (TranspNet)—0
Refining Translations with LLMs: A Constraint-Aware Iterative Prompting Approach—0
Towards Optimizing a Retrieval Augmented Generation using Large Language Model on Academic Data—0
A Large-Scale Study of Relevance Assessments with Large Language Models: An Initial LookCode0
Towards Evaluating Large Language Models for Graph Query Generation—0
Are LLMs Prescient? A Continuous Evaluation using Daily News as the Oracle—0
Trustful LLMs: Customizing and Grounding Text Generation with Knowledge Bases and Dual Decoders—0
Query Optimization for Parametric Knowledge Refinement in Retrieval-Augmented Large Language Models—0
Invar-RAG: Invariant LLM-aligned Retrieval for Better Generation—0
Toward Optimal Search and Retrieval for RAGCode0
OpenThaiGPT 1.5: A Thai-Centric Open Source Large Language Model—0
LProtector: An LLM-driven Vulnerability Detection System—0
Clustering Algorithms and RAG Enhancing Semi-Supervised Text Classification with Large LLMs—0
Leveraging Retrieval-Augmented Generation for Persian University Knowledge Retrieval—0
Exploring Knowledge Boundaries in Large Language Models for Retrieval Judgment—0
Sufficient Context: A New Lens on Retrieval Augmented Generation Systems—0
FinDVer: Explainable Claim Verification over Long and Hybrid-Content Financial DocumentsCode0
Enhancing Cluster Resilience: LLM-agent Based Autonomous Intelligent Cluster Diagnosis System and Evaluation Framework—0
AgentOps: Enabling Observability of LLM Agents—0
IntellBot: Retrieval Augmented LLM Chatbot for Cyber Threat Knowledge DeliveryCode0
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