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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 801–825 of 2111 papers

TitleStatusHype
AI Assistants to Enhance and Exploit the PETSc Knowledge Base—0
A Comprehensive Evaluation of Large Language Models on Temporal Event Forecasting—0
SAGE: A Framework of Precise Retrieval for RAG—0
Development of REGAI: Rubric Enabled Generative Artificial Intelligence—0
Correctness is not Faithfulness in RAG Attributions—0
Athena: Retrieval-augmented Legal Judgment Prediction with Large Language Models—0
AI Assistants for Spaceflight Procedures: Combining Generative Pre-Trained Transformer and Retrieval-Augmented Generation on Knowledge Graphs With Augmented Reality Cues—0
Corpus-informed Retrieval Augmented Generation of Clarifying Questions—0
CoRe-MMRAG: Cross-Source Knowledge Reconciliation for Multimodal RAG—0
CORD: Balancing COnsistency and Rank Distillation for Robust Retrieval-Augmented Generation—0
AgentOps: Enabling Observability of LLM Agents—0
AI Approaches to Qualitative and Quantitative News Analytics on NATO Unity—0
CoRAG: Collaborative Retrieval-Augmented Generation—0
CORAG: A Cost-Constrained Retrieval Optimization System for Retrieval-Augmented Generation—0
Conversational Text Extraction with Large Language Models Using Retrieval-Augmented Systems—0
How Does Knowledge Selection Help Retrieval Augmented Generation?—0
AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges—0
Conversation AI Dialog for Medicare powered by Finetuning and Retrieval Augmented Generation—0
Control Token with Dense Passage Retrieval—0
A Systematic Evaluation of LLM Strategies for Mental Health Text Analysis: Fine-tuning vs. Prompt Engineering vs. RAG—0
ControlNET: A Firewall for RAG-based LLM System—0
A Survey on Retrieval-Augmented Text Generation for Large Language Models—0
A Hybrid RAG System with Comprehensive Enhancement on Complex Reasoning—0
A Comparison of LLM Finetuning Methods & Evaluation Metrics with Travel Chatbot Use Case—0
Towards Efficient Educational Chatbots: Benchmarking RAG Frameworks—0
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