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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 951–1000 of 2111 papers

TitleStatusHype
Athena: Retrieval-augmented Legal Judgment Prediction with Large Language Models—0
Advanced System Integration: Analyzing OpenAPI Chunking for Retrieval-Augmented Generation—0
It's High Time: A Survey of Temporal Information Retrieval and Question Answering—0
Holistic Reasoning with Long-Context LMs: A Benchmark for Database Operations on Massive Textual Data—0
Homa at SemEval-2025 Task 5: Aligning Librarian Records with OntoAligner for Subject Tagging—0
Honest AI: Fine-Tuning "Small" Language Models to Say "I Don't Know", and Reducing Hallucination in RAG—0
Enhancing Retrieval for ESGLLM via ESG-CID -- A Disclosure Content Index Finetuning Dataset for Mapping GRI and ESRS—0
Annotating Speech, Attitude and Perception Reports—0
Enhancing Retrieval-Augmented LMs with a Two-stage Consistency Learning Compressor—0
Bridging the Preference Gap between Retrievers and LLMs—0
An LLM-Powered Clinical Calculator Chatbot Backed by Verifiable Clinical Calculators and their Metadata—0
How to Build an AI Tutor That Can Adapt to Any Course Using Knowledge Graph-Enhanced Retrieval-Augmented Generation (KG-RAG)—0
Enhancing RAG with Active Learning on Conversation Records: Reject Incapables and Answer Capables—0
Automating Pharmacovigilance Evidence Generation: Using Large Language Models to Produce Context-Aware SQL—0
Enhancing Q&A with Domain-Specific Fine-Tuning and Iterative Reasoning: A Comparative Study—0
Enhancing Q&A Text Retrieval with Ranking Models: Benchmarking, fine-tuning and deploying Rerankers for RAG—0
Human-Calibrated Automated Testing and Validation of Generative Language Models—0
Human Cognition Inspired RAG with Knowledge Graph for Complex Problem Solving—0
Bridging the Gap: Enabling Natural Language Queries for NoSQL Databases through Text-to-NoSQL Translation—0
Advanced Real-Time Fraud Detection Using RAG-Based LLMs—0
JudgeRank: Leveraging Large Language Models for Reasoning-Intensive Reranking—0
Hybrid AI for Responsive Multi-Turn Online Conversations with Novel Dynamic Routing and Feedback Adaptation—0
Hybrid RAG-empowered Multi-modal LLM for Secure Data Management in Internet of Medical Things: A Diffusion-based Contract Approach—0
HybridRAG: Integrating Knowledge Graphs and Vector Retrieval Augmented Generation for Efficient Information Extraction—0
Hybrid-SQuAD: Hybrid Scholarly Question Answering Dataset—0
Hybrid Student-Teacher Large Language Model Refinement for Cancer Toxicity Symptom Extraction—0
HyPA-RAG: A Hybrid Parameter Adaptive Retrieval-Augmented Generation System for AI Legal and Policy Applications—0
Enhancing Pancreatic Cancer Staging with Large Language Models: The Role of Retrieval-Augmented Generation—0
Cross-Data Knowledge Graph Construction for LLM-enabled Educational Question-Answering System: A Case Study at HCMUT—0
Enhancing Online Learning Efficiency Through Heterogeneous Resource Integration with a Multi-Agent RAG System—0
Hyper-RAG: Combating LLM Hallucinations using Hypergraph-Driven Retrieval-Augmented Generation—0
HyperRAG: Enhancing Quality-Efficiency Tradeoffs in Retrieval-Augmented Generation with Reranker KV-Cache Reuse—0
IAG: Induction-Augmented Generation Framework for Answering Reasoning Questions—0
ICLERB: In-Context Learning Embedding and Reranker Benchmark—0
Identifying Performance-Sensitive Configurations in Software Systems through Code Analysis with LLM Agents—0
AI for Climate Finance: Agentic Retrieval and Multi-Step Reasoning for Early Warning System Investments—0
Bridging the Gap: Dynamic Learning Strategies for Improving Multilingual Performance in LLMs—0
ImageRAG: Dynamic Image Retrieval for Reference-Guided Image Generation—0
Enhancing Multilingual Information Retrieval in Mixed Human Resources Environments: A RAG Model Implementation for Multicultural Enterprise—0
An LLM Agent for Automatic Geospatial Data Analysis—0
Advanced RAG Models with Graph Structures: Optimizing Complex Knowledge Reasoning and Text Generation—0
Enhancing Long Context Performance in LLMs Through Inner Loop Query Mechanism—0
Improving Factuality of 3D Brain MRI Report Generation with Paired Image-domain Retrieval and Text-domain Augmentation—0
Improving Factuality with Explicit Working Memory—0
CUE-M: Contextual Understanding and Enhanced Search with Multimodal Large Language Model—0
Enhancing LLMs for Power System Simulations: A Feedback-driven Multi-agent Framework—0
Improving Multimodal LLMs Ability In Geometry Problem Solving, Reasoning, And Multistep Scoring—0
Current state of LLM Risks and AI Guardrails—0
Bridging Relevance and Reasoning: Rationale Distillation in Retrieval-Augmented Generation—0
Enhancing LLM Intelligence with ARM-RAG: Auxiliary Rationale Memory for Retrieval Augmented Generation—0
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