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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 951–975 of 2111 papers

TitleStatusHype
GARLIC: LLM-Guided Dynamic Progress Control with Hierarchical Weighted Graph for Long Document QA—0
CollEX -- A Multimodal Agentic RAG System Enabling Interactive Exploration of Scientific Collections—0
GAIA: A General AI Assistant for Intelligent Accelerator Operations—0
Holistic Reasoning with Long-Context LMs: A Benchmark for Database Operations on Massive Textual Data—0
Collapse of Dense Retrievers: Short, Early, and Literal Biases Outranking Factual Evidence—0
Honest AI: Fine-Tuning "Small" Language Models to Say "I Don't Know", and Reducing Hallucination in RAG—0
A Socratic RAG Approach to Connect Natural Language Queries on Research Topics with Knowledge Organization Systems—0
Accurate and Energy Efficient: Local Retrieval-Augmented Generation Models Outperform Commercial Large Language Models in Medical Tasks—0
FunnelRAG: A Coarse-to-Fine Progressive Retrieval Paradigm for RAG—0
From Standalone LLMs to Integrated Intelligence: A Survey of Compound Al Systems—0
From RAG to RICHES: Retrieval Interlaced with Sequence Generation—0
How to Build an AI Tutor That Can Adapt to Any Course Using Knowledge Graph-Enhanced Retrieval-Augmented Generation (KG-RAG)—0
Cognitive-Aligned Document Selection for Retrieval-augmented Generation—0
From RAG to Agentic: Validating Islamic-Medicine Responses with LLM Agents—0
From RAGs to rich parameters: Probing how language models utilize external knowledge over parametric information for factual queries—0
From RAGs to riches: Using large language models to write documents for clinical trials—0
From Questions to Insightful Answers: Building an Informed Chatbot for University Resources—0
Human Cognition Inspired RAG with Knowledge Graph for Complex Problem Solving—0
CodeXEmbed: A Generalist Embedding Model Family for Multiligual and Multi-task Code Retrieval—0
Ask-EDA: A Design Assistant Empowered by LLM, Hybrid RAG and Abbreviation De-hallucination—0
From PowerPoint UI Sketches to Web-Based Applications: Pattern-Driven Code Generation for GIS Dashboard Development Using Knowledge-Augmented LLMs, Context-Aware Visual Prompting, and the React Framework—0
A Simple Architecture for Enterprise Large Language Model Applications based on Role based security and Clearance Levels using Retrieval-Augmented Generation or Mixture of Experts—0
Agentic Retrieval-Augmented Generation for Time Series Analysis—0
HybridRAG: Integrating Knowledge Graphs and Vector Retrieval Augmented Generation for Efficient Information Extraction—0
Accommodate Knowledge Conflicts in Retrieval-augmented LLMs: Towards Reliable Response Generation in the Wild—0
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