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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
VERA: Validation and Evaluation of Retrieval-Augmented Systems—0
Plan with Code: Comparing approaches for robust NL to DSL generation—0
WeKnow-RAG: An Adaptive Approach for Retrieval-Augmented Generation Integrating Web Search and Knowledge Graphs—0
Exploring Retrieval Augmented Generation in ArabicCode0
Optimizing RAG Techniques for Automotive Industry PDF Chatbots: A Case Study with Locally Deployed Ollama Models—0
A New Pipeline For Generating Instruction Dataset via RAG and Self Fine-Tuning—0
Bayesian inference to improve quality of Retrieval Augmented Generation—0
HybridRAG: Integrating Knowledge Graphs and Vector Retrieval Augmented Generation for Efficient Information Extraction—0
Retrieval-augmented code completion for local projects using large language models—0
A Hybrid RAG System with Comprehensive Enhancement on Complex Reasoning—0
Rag and Roll: An End-to-End Evaluation of Indirect Prompt Manipulations in LLM-based Application Frameworks—0
FiSTECH: Financial Style Transfer to Enhance Creativity without Hallucinations in LLMs—0
ConfusedPilot: Confused Deputy Risks in RAG-based LLMs—0
Hybrid Student-Teacher Large Language Model Refinement for Cancer Toxicity Symptom Extraction—0
Towards Explainable Network Intrusion Detection using Large Language Models—0
ACL Ready: RAG Based Assistant for the ACL ChecklistCode0
Large Language Model as a Catalyst: A Paradigm Shift in Base Station Siting Optimization—0
MaxMind: A Memory Loop Network to Enhance Software Productivity based on Large Language Models—0
A Comparison of LLM Finetuning Methods & Evaluation Metrics with Travel Chatbot Use Case—0
FLASH: Federated Learning-Based LLMs for Advanced Query Processing in Social Networks through RAG—0
KnowPO: Knowledge-aware Preference Optimization for Controllable Knowledge Selection in Retrieval-Augmented Language Models—0
LLM-based MOFs Synthesis Condition Extraction using Few-Shot Demonstrations—0
AppAgent v2: Advanced Agent for Flexible Mobile Interactions—0
Development of REGAI: Rubric Enabled Generative Artificial Intelligence—0
Wiping out the limitations of Large Language Models -- A Taxonomy for Retrieval Augmented Generation—0
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