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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 1151–1175 of 2111 papers

TitleStatusHype
Chatbot Arena Meets Nuggets: Towards Explanations and Diagnostics in the Evaluation of LLM Responses—0
DFA-RAG: Conversational Semantic Router for Large Language Model with Definite Finite Automaton—0
Chatmap : Large Language Model Interaction with Cartographic Data—0
ChatQA 2: Bridging the Gap to Proprietary LLMs in Long Context and RAG Capabilities—0
ChatQA: Surpassing GPT-4 on Conversational QA and RAG—0
Chats-Grid: An Iterative Retrieval Q&A Optimization Scheme Leveraging Large Model and Retrieval Enhancement Generation in smart grid—0
Chinese SafetyQA: A Safety Short-form Factuality Benchmark for Large Language Models—0
CHORUS: Zero-shot Hierarchical Retrieval and Orchestration for Generating Linear Programming Code—0
ChunkRAG: Novel LLM-Chunk Filtering Method for RAG Systems—0
Chunk Twice, Embed Once: A Systematic Study of Segmentation and Representation Trade-offs in Chemistry-Aware Retrieval-Augmented Generation—0
CiteFix: Enhancing RAG Accuracy Through Post-Processing Citation Correction—0
ClaimTrust: Propagation Trust Scoring for RAG Systems—0
Claim Verification in the Age of Large Language Models: A Survey—0
Classifying Peace in Global Media Using RAG and Intergroup Reciprocity—0
Class-RAG: Real-Time Content Moderation with Retrieval Augmented Generation—0
CLI-RAG: A Retrieval-Augmented Framework for Clinically Structured and Context Aware Text Generation with LLMs—0
CL-RAG: Bridging the Gap in Retrieval-Augmented Generation with Curriculum Learning—0
Clustering Algorithms and RAG Enhancing Semi-Supervised Text Classification with Large LLMs—0
Code Graph Model (CGM): A Graph-Integrated Large Language Model for Repository-Level Software Engineering Tasks—0
CodeXEmbed: A Generalist Embedding Model Family for Multiligual and Multi-task Code Retrieval—0
Cognitive-Aligned Document Selection for Retrieval-augmented Generation—0
Collapse of Dense Retrievers: Short, Early, and Literal Biases Outranking Factual Evidence—0
CollEX -- A Multimodal Agentic RAG System Enabling Interactive Exploration of Scientific Collections—0
Column Vocabulary Association (CVA): semantic interpretation of dataless tables—0
Combining Domain-Specific Models and LLMs for Automated Disease Phenotyping from Survey Data—0
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