SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1801–1850 of 2111 papers

TitleStatusHype
AI-native Memory: A Pathway from LLMs Towards AGI—0
"Glue pizza and eat rocks" -- Exploiting Vulnerabilities in Retrieval-Augmented Generative Models—0
Evaluating Quality of Answers for Retrieval-Augmented Generation: A Strong LLM Is All You Need—0
Multi-step Inference over Unstructured Data—0
RAGBench: Explainable Benchmark for Retrieval-Augmented Generation Systems—0
Attention Instruction: Amplifying Attention in the Middle via PromptingCode0
On the Role of Long-tail Knowledge in Retrieval Augmented Large Language Models—0
Context-augmented Retrieval: A Novel Framework for Fast Information Retrieval based Response Generation using Large Language Model—0
Graph-Augmented LLMs for Personalized Health Insights: A Case Study in Sleep Analysis—0
FS-RAG: A Frame Semantics Based Approach for Improved Factual Accuracy in Large Language ModelsCode0
Found in the Middle: Calibrating Positional Attention Bias Improves Long Context Utilization—0
Retrieve-Plan-Generation: An Iterative Planning and Answering Framework for Knowledge-Intensive LLM GenerationCode0
LongRAG: Enhancing Retrieval-Augmented Generation with Long-context LLMs—0
TemPrompt: Multi-Task Prompt Learning for Temporal Relation Extraction in RAG-based Crowdsourcing Systems—0
Integrating Knowledge Retrieval and Large Language Models for Clinical Report Correction—0
Pistis-RAG: Enhancing Retrieval-Augmented Generation with Human Feedback—0
Towards Retrieval Augmented Generation over Large Video Libraries—0
A Tale of Trust and Accuracy: Base vs. Instruct LLMs in RAG SystemsCode0
TTQA-RS- A break-down prompting approach for Multi-hop Table-Text Question Answering with Reasoning and Summarization—0
Relation Extraction with Fine-Tuned Large Language Models in Retrieval Augmented Generation Frameworks—0
DIRAS: Efficient LLM Annotation of Document Relevance in Retrieval Augmented GenerationCode0
QPaug: Question and Passage Augmentation for Open-Domain Question Answering of LLMsCode0
Thread: A Logic-Based Data Organization Paradigm for How-To Question Answering with Retrieval Augmented Generation—0
Improving Zero-shot LLM Re-Ranker with Risk Minimization—0
WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia—0
FoRAG: Factuality-optimized Retrieval Augmented Generation for Web-enhanced Long-form Question Answering—0
From RAGs to rich parameters: Probing how language models utilize external knowledge over parametric information for factual queries—0
Query Routing for Homogeneous Tools: An Instantiation in the RAG Scenario—0
RichRAG: Crafting Rich Responses for Multi-faceted Queries in Retrieval-Augmented Generation—0
Retrieval-Augmented Generation for Generative Artificial Intelligence in Medicine—0
Debate as Optimization: Adaptive Conformal Prediction and Diverse Retrieval for Event Extraction—0
Identifying Performance-Sensitive Configurations in Software Systems through Code Analysis with LLM Agents—0
Retrieval Meets Reasoning: Dynamic In-Context Editing for Long-Text Understanding—0
Intermediate Distillation: Data-Efficient Distillation from Black-Box LLMs for Information Retrieval—0
Iterative Utility Judgment Framework via LLMs Inspired by Relevance in Philosophy—0
CrAM: Credibility-Aware Attention Modification in LLMs for Combating Misinformation in RAGCode0
Fine-Tuning or Fine-Failing? Debunking Performance Myths in Large Language Models—0
Retrieval-Augmented Feature Generation for Domain-Specific Classification—0
Evaluating the Efficacy of Open-Source LLMs in Enterprise-Specific RAG Systems: A Comparative Study of Performance and ScalabilityCode0
Refiner: Restructure Retrieval Content Efficiently to Advance Question-Answering CapabilitiesCode0
Vul-RAG: Enhancing LLM-based Vulnerability Detection via Knowledge-level RAG—0
SeRTS: Self-Rewarding Tree Search for Biomedical Retrieval-Augmented Generation—0
Satyrn: A Platform for Analytics Augmented GenerationCode0
RAEmoLLM: Retrieval Augmented LLMs for Cross-Domain Misinformation Detection Using In-Context Learning based on Emotional InformationCode0
Current state of LLM Risks and AI Guardrails—0
Automating Pharmacovigilance Evidence Generation: Using Large Language Models to Produce Context-Aware SQL—0
HIRO: Hierarchical Information Retrieval OptimizationCode0
ClimRetrieve: A Benchmarking Dataset for Information Retrieval from Corporate Climate DisclosuresCode0
Battling Botpoop using GenAI for Higher Education: A Study of a Retrieval Augmented Generation Chatbots Impact on Learning—0
Exploring Fact Memorization and Style Imitation in LLMs Using QLoRA: An Experimental Study and Quality Assessment Methods—0
Show:102550
← PrevPage 37 of 43Next →

No leaderboard results yet.