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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 1126–1150 of 2111 papers

TitleStatusHype
Leveraging LLM Agents for Automated Optimization Modeling for SASP Problems: A Graph-RAG based Approach—0
GLLM: Self-Corrective G-Code Generation using Large Language Models with User Feedback—0
Leveraging In-Context Learning and Retrieval-Augmented Generation for Automatic Question Generation in Educational Domains—0
ASTRAL: Automated Safety Testing of Large Language Models—0
Balancing Content Size in RAG-Text2SQL System—0
Multiple Abstraction Level Retrieve Augment Generation—0
Open-Source Retrieval Augmented Generation Framework for Retrieving Accurate Medication Insights from Formularies for African Healthcare Workers—0
Enhanced Retrieval of Long Documents: Leveraging Fine-Grained Block Representations with Large Language Models—0
Implementation of a Generative AI Assistant in K-12 Education: The CyberScholar Initiative—0
Raiders of the Lost Dependency: Fixing Dependency Conflicts in Python using LLMs—0
Characterizing Network Structure of Anti-Trans Actors on TikTok—0
LemmaHead: RAG Assisted Proof Generation Using Large Language Models—0
Provence: efficient and robust context pruning for retrieval-augmented generation—0
PISCO: Pretty Simple Compression for Retrieval-Augmented Generation—0
URAG: Implementing a Unified Hybrid RAG for Precise Answers in University Admission Chatbots -- A Case Study at HCMUT—0
SEAL: Speech Embedding Alignment Learning for Speech Large Language Model with Retrieval-Augmented Generation—0
CG-RAG: Research Question Answering by Citation Graph Retrieval-Augmented LLMs—0
Enhancing Intent Understanding for Ambiguous prompt: A Human-Machine Co-Adaption Strategy—0
ASRank: Zero-Shot Re-Ranking with Answer Scent for Document Retrieval—0
Advanced Real-Time Fraud Detection Using RAG-Based LLMs—0
An AI-Driven Live Systematic Reviews in the Brain-Heart Interconnectome: Minimizing Research Waste and Advancing Evidence SynthesisCode0
Federated Retrieval Augmented Generation for Multi-Product Question Answering—0
Chain-of-Retrieval Augmented Generation—0
GraPPI: A Retrieve-Divide-Solve GraphRAG Framework for Large-scale Protein-protein Interaction ExplorationCode0
Causal Graphs Meet Thoughts: Enhancing Complex Reasoning in Graph-Augmented LLMsCode0
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