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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
Enhancing Large Language Models (LLMs) for Telecommunications using Knowledge Graphs and Retrieval-Augmented Generation—0
Hyper-RAG: Combating LLM Hallucinations using Hypergraph-Driven Retrieval-Augmented Generation—0
GRASP: Municipal Budget AI Chatbots for Enhancing Civic Engagement—0
SCORE: Story Coherence and Retrieval Enhancement for AI Narratives—0
SalesRLAgent: A Reinforcement Learning Approach for Real-Time Sales Conversion Prediction and Optimization—0
MHTS: Multi-Hop Tree Structure Framework for Generating Difficulty-Controllable QA Datasets for RAG Evaluation—0
Citegeist: Automated Generation of Related Work Analysis on the arXiv CorpusCode0
Memory-Aware and Uncertainty-Guided Retrieval for Multi-Hop Question Answering—0
DAT: Dynamic Alpha Tuning for Hybrid Retrieval in Retrieval-Augmented Generation—0
Understanding Inequality of LLM Fact-Checking over Geographic Regions with Agent and Retrieval models—0
Real-Time Evaluation Models for RAG: Who Detects Hallucinations Best?—0
MemInsight: Autonomous Memory Augmentation for LLM Agents—0
Tricking Retrievers with Influential Tokens: An Efficient Black-Box Corpus Poisoning Attack—0
AutoPsyC: Automatic Recognition of Psychodynamic Conflicts from Semi-structured Interviews with Large Language Models—0
A Survey of Multimodal Retrieval-Augmented Generation—0
Reasoning Beyond Limits: Advances and Open Problems for LLMs—0
RALLRec+: Retrieval Augmented Large Language Model Recommendation with ReasoningCode0
Dewey Long Context Embedding Model: A Technical Report—0
CausalRAG: Integrating Causal Graphs into Retrieval-Augmented Generation—0
Fact-checking AI-generated news reports: Can LLMs catch their own lies?—0
Improving RAG for Personalization with Author Features and Contrastive ExamplesCode0
Synthetic Function Demonstrations Improve Generation in Low-Resource Programming Languages—0
GridMind: A Multi-Agent NLP Framework for Unified, Cross-Modal NFL Data Insights—0
MedPlan:A Two-Stage RAG-Based System for Personalized Medical Plan Generation—0
GINGER: Grounded Information Nugget-Based Generation of ResponsesCode0
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