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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 1851–1875 of 2111 papers

TitleStatusHype
Tabular Embedding Model (TEM): Finetuning Embedding Models For Tabular RAG Applications—0
Generative AI for Low-Carbon Artificial Intelligence of Things with Large Language Models—0
Tool Calling: Enhancing Medication Consultation via Retrieval-Augmented Large Language Models—0
Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answering—0
Human-Imperceptible Retrieval Poisoning Attacks in LLM-Powered Applications—0
From Local to Global: A Graph RAG Approach to Query-Focused SummarizationCode14
Prompt Leakage effect and defense strategies for multi-turn LLM interactions—0
Studying Large Language Model Behaviors Under Context-Memory Conflicts With Real DocumentsCode1
Telco-RAG: Navigating the Challenges of Retrieval-Augmented Language Models for TelecommunicationsCode2
IryoNLP at MEDIQA-CORR 2024: Tackling the Medical Error Detection & Correction Task On the Shoulders of Medical Agents—0
Typos that Broke the RAG's Back: Genetic Attack on RAG Pipeline by Simulating Documents in the Wild via Low-level PerturbationsCode0
LLMs Know What They Need: Leveraging a Missing Information Guided Framework to Empower Retrieval-Augmented GenerationCode1
Retrieval-Augmented Audio Deepfake Detection—0
Evaluating Retrieval Quality in Retrieval-Augmented GenerationCode1
Dubo-SQL: Diverse Retrieval-Augmented Generation and Fine Tuning for Text-to-SQLCode1
Unlocking Multi-View Insights in Knowledge-Dense Retrieval-Augmented Generation—0
RAGCache: Efficient Knowledge Caching for Retrieval-Augmented Generation—0
RAGAR, Your Falsehood Radar: RAG-Augmented Reasoning for Political Fact-Checking using Multimodal Large Language Models—0
LongEmbed: Extending Embedding Models for Long Context RetrievalCode2
RAM: Towards an Ever-Improving Memory System by Learning from Communications—0
iRAG: Advancing RAG for Videos with an Incremental Approach—0
MemLLM: Finetuning LLMs to Use An Explicit Read-Write MemoryCode1
Enhancing Q&A with Domain-Specific Fine-Tuning and Iterative Reasoning: A Comparative Study—0
A Survey on Retrieval-Augmented Text Generation for Large Language Models—0
Position Engineering: Boosting Large Language Models through Positional Information Manipulation—0
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