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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 876–900 of 2111 papers

TitleStatusHype
Comprehensive and Practical Evaluation of Retrieval-Augmented Generation Systems for Medical Question Answering—0
GeoRAG: A Question-Answering Approach from a Geographical Perspective—0
ERATTA: Extreme RAG for Table To Answers with Large Language Models—0
CAISSON: Concept-Augmented Inference Suite of Self-Organizing Neural Networks—0
How Much Can RAG Help the Reasoning of LLM?—0
GLLM: Self-Corrective G-Code Generation using Large Language Models with User Feedback—0
"Glue pizza and eat rocks" -- Exploiting Vulnerabilities in Retrieval-Augmented Generative Models—0
GNN-ACLP: Graph Neural Networks based Analog Circuit Link Prediction—0
CAFE: Retrieval Head-based Coarse-to-Fine Information Seeking to Enhance Multi-Document QA Capability—0
ENWAR: A RAG-empowered Multi-Modal LLM Framework for Wireless Environment Perception—0
GPT-4 as an Agronomist Assistant? Answering Agriculture Exams Using Large Language Models—0
EnronQA: Towards Personalized RAG over Private Documents—0
Cache-Craft: Managing Chunk-Caches for Efficient Retrieval-Augmented Generation—0
Anveshana: A New Benchmark Dataset for Cross-Lingual Information Retrieval On English Queries and Sanskrit Documents—0
Enhancing tutoring systems by leveraging tailored promptings and domain knowledge with Large Language Models—0
Enhancing Tourism Recommender Systems for Sustainable City Trips Using Retrieval-Augmented Generation—0
Enhancing Thyroid Cytology Diagnosis with RAG-Optimized LLMs and Pa-thology Foundation Models—0
C-3PO: Compact Plug-and-Play Proxy Optimization to Achieve Human-like Retrieval-Augmented Generation—0
GraphAide: Advanced Graph-Assisted Query and Reasoning System—0
Answering real-world clinical questions using large language model based systems—0
Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Survey—0
Advancing Conversational Psychotherapy: Integrating Privacy, Dual-Memory, and Domain Expertise with Large Language Models—0
Graph-Based Retriever Captures the Long Tail of Biomedical Knowledge—0
How to Build an AI Tutor That Can Adapt to Any Course Using Knowledge Graph-Enhanced Retrieval-Augmented Generation (KG-RAG)—0
Building Trustworthy AI: Transparent AI Systems via Large Language Models, Ontologies, and Logical Reasoning (TranspNet)—0
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