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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 1676–1700 of 2111 papers

TitleStatusHype
Optimizing Query Generation for Enhanced Document Retrieval in RAG—0
Optimizing RAG Techniques for Automotive Industry PDF Chatbots: A Case Study with Locally Deployed Ollama Models—0
Optimizing Retrieval-Augmented Generation with Elasticsearch for Enhanced Question-Answering Systems—0
Optimizing Retrieval-Augmented Generation: Analysis of Hyperparameter Impact on Performance and Efficiency—0
Optimizing Retrieval Augmented Generation for Object Constraint Language—0
Optimizing Retrieval-Augmented Generation of Medical Content for Spaced Repetition Learning—0
ORANSight-2.0: Foundational LLMs for O-RAN—0
ORAssistant: A Custom RAG-based Conversational Assistant for OpenROAD—0
Oreo: A Plug-in Context Reconstructor to Enhance Retrieval-Augmented Generation—0
oRetrieval Augmented Generation for 10 Large Language Models and its Generalizability in Assessing Medical Fitness—0
OrthoDoc: Multimodal Large Language Model for Assisting Diagnosis in Computed Tomography—0
OSCAR: Online Soft Compression And Reranking—0
Osiris: A Lightweight Open-Source Hallucination Detection System—0
Overcoming LLM Challenges using RAG-Driven Precision in Coffee Leaf Disease Remediation—0
PaperHelper: Knowledge-Based LLM QA Paper Reading Assistant—0
PaperQA: Retrieval-Augmented Generative Agent for Scientific Research—0
Parallel Key-Value Cache Fusion for Position Invariant RAG—0
Parenting: Optimizing Knowledge Selection of Retrieval-Augmented Language Models with Parameter Decoupling and Tailored Tuning—0
ParetoRAG: Leveraging Sentence-Context Attention for Robust and Efficient Retrieval-Augmented Generation—0
Passage Segmentation of Documents for Extractive Question Answering—0
Patchwork: A Unified Framework for RAG Serving—0
PathGPT: Leveraging Large Language Models for Personalized Route Generation—0
PAT-Questions: A Self-Updating Benchmark for Present-Anchored Temporal Question-Answering—0
PCA-RAG: Principal Component Analysis for Efficient Retrieval-Augmented Generation—0
PEAR: Position-Embedding-Agnostic Attention Re-weighting Enhances Retrieval-Augmented Generation with Zero Inference Overhead—0
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