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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 1101–1125 of 2111 papers

TitleStatusHype
DeepThink: Aligning Language Models with Domain-Specific User Intents—0
Efficient Knowledge Feeding to Language Models: A Novel Integrated Encoder-Decoder Architecture—0
Agentic Reasoning: Reasoning LLMs with Tools for the Deep ResearchCode0
Enhancing Health Information Retrieval with RAG by Prioritizing Topical Relevance and Factual Accuracy—0
RAG-Verus: Repository-Level Program Verification with LLMs using Retrieval Augmented Generation—0
LLMs to Support a Domain Specific Knowledge Assistant—0
Experiments with Large Language Models on Retrieval-Augmented Generation for Closed-Source Simulation Software—0
Enhancing Online Learning Efficiency Through Heterogeneous Resource Integration with a Multi-Agent RAG System—0
MARAGE: Transferable Multi-Model Adversarial Attack for Retrieval-Augmented Generation Data Extraction—0
Cache-Craft: Managing Chunk-Caches for Efficient Retrieval-Augmented Generation—0
Spatial-RAG: Spatial Retrieval Augmented Generation for Real-World Geospatial Reasoning Questions—0
LLMSecConfig: An LLM-Based Approach for Fixing Software Container Misconfigurations—0
SCALM: Detecting Bad Practices in Smart Contracts Through LLMs—0
Personalization Toolkit: Training Free Personalization of Large Vision Language Models—0
Conversation AI Dialog for Medicare powered by Finetuning and Retrieval Augmented Generation—0
Open Foundation Models in Healthcare: Challenges, Paradoxes, and Opportunities with GenAI Driven Personalized Prescription—0
Topic-FlipRAG: Topic-Orientated Adversarial Opinion Manipulation Attacks to Retrieval-Augmented Generation Models—0
Knowledge Synthesis of Photosynthesis Research Using a Large Language Model—0
BARE: Leveraging Base Language Models for Few-Shot Synthetic Data Generation—0
Al-Khwarizmi: Discovering Physical Laws with Foundation Models—0
DeepRAG: Thinking to Retrieval Step by Step for Large Language Models—0
RealRAG: Retrieval-augmented Realistic Image Generation via Self-reflective Contrastive Learning—0
LLM-based event log analysis techniques: A survey—0
Can we Retrieve Everything All at Once? ARM: An Alignment-Oriented LLM-based Retrieval Method—0
Retrieval Augmented Generation Based LLM Evaluation For Protocol State Machine Inference With Chain-of-Thought Reasoning—0
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