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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 801–850 of 2111 papers

TitleStatusHype
Fine-Tuning or Fine-Failing? Debunking Performance Myths in Large Language Models—0
Fine-Tuning or Retrieval? Comparing Knowledge Injection in LLMs—0
Grounding Language Model with Chunking-Free In-Context Retrieval—0
FinRAGBench-V: A Benchmark for Multimodal RAG with Visual Citation in the Financial Domain—0
FinSage: A Multi-aspect RAG System for Financial Filings Question Answering—0
FinTextQA: A Dataset for Long-form Financial Question Answering—0
FinTMMBench: Benchmarking Temporal-Aware Multi-Modal RAG in Finance—0
First Token Probability Guided RAG for Telecom Question Answering—0
Gumbel Reranking: Differentiable End-to-End Reranker Optimization—0
FiSTECH: Financial Style Transfer to Enhance Creativity without Hallucinations in LLMs—0
FIT-RAG: Black-Box RAG with Factual Information and Token Reduction—0
FLASH: Federated Learning-Based LLMs for Advanced Query Processing in Social Networks through RAG—0
FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Research—0
Hallucinations and Truth: A Comprehensive Accuracy Evaluation of RAG, LoRA and DoRA—0
GenDFIR: Advancing Cyber Incident Timeline Analysis Through Retrieval Augmented Generation and Large Language Models—0
Flippi: End To End GenAI Assistant for E-Commerce—0
Augmenting Large Language Models with Static Code Analysis for Automated Code Quality Improvements—0
Evaluating and Enhancing Large Language Models Performance in Domain-specific Medicine: Osteoarthritis Management with DocOA—0
CALLM: Understanding Cancer Survivors' Emotions and Intervention Opportunities via Mobile Diaries and Context-Aware Language Models—0
Formal Language Knowledge Corpus for Retrieval Augmented Generation—0
CLI-RAG: A Retrieval-Augmented Framework for Clinically Structured and Context Aware Text Generation with LLMs—0
Fortify Your Foundations: Practical Privacy and Security for Foundation Model Deployments In The Cloud—0
Found in the Middle: Calibrating Positional Attention Bias Improves Long Context Utilization—0
FRAG: A Flexible Modular Framework for Retrieval-Augmented Generation based on Knowledge Graphs—0
FreshStack: Building Realistic Benchmarks for Evaluating Retrieval on Technical Documents—0
From Code Generation to Software Testing: AI Copilot with Context-Based RAG—0
A Pilot Empirical Study on When and How to Use Knowledge Graphs as Retrieval Augmented Generation—0
Clustering Algorithms and RAG Enhancing Semi-Supervised Text Classification with Large LLMs—0
From "Hallucination" to "Suture": Insights from Language Philosophy to Enhance Large Language Models—0
Establishing Performance Baselines in Fine-Tuning, Retrieval-Augmented Generation and Soft-Prompting for Non-Specialist LLM Users—0
ESGReveal: An LLM-based approach for extracting structured data from ESG reports—0
Calibrated Decision-Making through LLM-Assisted Retrieval—0
ER-RAG: Enhance RAG with ER-Based Unified Modeling of Heterogeneous Data Sources—0
ERATTA: Extreme RAG for Table To Answers with Large Language Models—0
From Questions to Insightful Answers: Building an Informed Chatbot for University Resources—0
From RAGs to riches: Using large language models to write documents for clinical trials—0
CAISSON: Concept-Augmented Inference Suite of Self-Organizing Neural Networks—0
From RAG to Agentic: Validating Islamic-Medicine Responses with LLM Agents—0
Graphy'our Data: Towards End-to-End Modeling, Exploring and Generating Report from Raw Data—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
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
GAIA: A General AI Assistant for Intelligent Accelerator Operations—0
Enhancing Thyroid Cytology Diagnosis with RAG-Optimized LLMs and Pa-thology Foundation Models—0
GARLIC: LLM-Guided Dynamic Progress Control with Hierarchical Weighted Graph for Long Document QA—0
C-3PO: Compact Plug-and-Play Proxy Optimization to Achieve Human-like Retrieval-Augmented Generation—0
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