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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 576–600 of 2111 papers

TitleStatusHype
ConceptFormer: Towards Efficient Use of Knowledge-Graph Embeddings in Large Language Models—0
Conan-embedding: General Text Embedding with More and Better Negative Samples—0
A Study on the Implementation Method of an Agent-Based Advanced RAG System Using Graph—0
A Collaborative Multi-Agent Approach to Retrieval-Augmented Generation Across Diverse Data—0
Enhancing RAG with Active Learning on Conversation Records: Reject Incapables and Answer Capables—0
Compressing Long Context for Enhancing RAG with AMR-based Concept Distillation—0
Comprehensive and Practical Evaluation of Retrieval-Augmented Generation Systems for Medical Question Answering—0
ASTRID -- An Automated and Scalable TRIaD for the Evaluation of RAG-based Clinical Question Answering Systems—0
Composing Open-domain Vision with RAG for Ocean Monitoring and Conservation—0
Complex System Diagnostics Using a Knowledge Graph-Informed and Large Language Model-Enhanced Framework—0
ASTRAL: Automated Safety Testing of Large Language Models—0
Comparing the Utility, Preference, and Performance of Course Material Search Functionality and Retrieval-Augmented Generation Large Language Model (RAG-LLM) AI Chatbots in Information-Seeking Tasks—0
Comparative Analysis of Retrieval Systems in the Real World—0
Assessing the Robustness of Retrieval-Augmented Generation Systems in K-12 Educational Question Answering with Knowledge Discrepancies—0
AgentNet: Decentralized Evolutionary Coordination for LLM-based Multi-Agent Systems—0
BadJudge: Backdoor Vulnerabilities of LLM-as-a-Judge—0
Pseudo-Knowledge Graph: Meta-Path Guided Retrieval and In-Graph Text for RAG-Equipped LLM—0
Assessing the Performance of Human-Capable LLMs -- Are LLMs Coming for Your Job?—0
Command A: An Enterprise-Ready Large Language Model—0
Agentic Verification for Ambiguous Query Disambiguation—0
Combining Domain-Specific Models and LLMs for Automated Disease Phenotyping from Survey Data—0
Assessing generalization capability of text ranking models in Polish—0
Accommodate Knowledge Conflicts in Retrieval-augmented LLMs: Towards Reliable Response Generation in the Wild—0
Column Vocabulary Association (CVA): semantic interpretation of dataless tables—0
ASRank: Zero-Shot Re-Ranking with Answer Scent for Document Retrieval—0
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