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 1701–1750 of 2111 papers

TitleStatusHype
Thread: A Logic-Based Data Organization Paradigm for How-To Question Answering with Retrieval Augmented Generation—0
Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More?Code3
WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia—0
R^2AG: Incorporating Retrieval Information into Retrieval Augmented GenerationCode1
RichRAG: Crafting Rich Responses for Multi-faceted Queries in Retrieval-Augmented Generation—0
Unified Active Retrieval for Retrieval Augmented GenerationCode1
Intermediate Distillation: Data-Efficient Distillation from Black-Box LLMs for Information Retrieval—0
PlanRAG: A Plan-then-Retrieval Augmented Generation for Generative Large Language Models as Decision MakersCode2
Debate as Optimization: Adaptive Conformal Prediction and Diverse Retrieval for Event Extraction—0
From RAGs to rich parameters: Probing how language models utilize external knowledge over parametric information for factual queries—0
Identifying Performance-Sensitive Configurations in Software Systems through Code Analysis with LLM Agents—0
Retrieval-Augmented Generation for Generative Artificial Intelligence in Medicine—0
Query Routing for Homogeneous Tools: An Instantiation in the RAG Scenario—0
Retrieval Meets Reasoning: Dynamic In-Context Editing for Long-Text Understanding—0
Satyrn: A Platform for Analytics Augmented GenerationCode0
SeRTS: Self-Rewarding Tree Search for Biomedical Retrieval-Augmented Generation—0
CrAM: Credibility-Aware Attention Modification in LLMs for Combating Misinformation in RAGCode0
Retrieval-Augmented Feature Generation for Domain-Specific Classification—0
TRACE the Evidence: Constructing Knowledge-Grounded Reasoning Chains for Retrieval-Augmented GenerationCode1
Iterative Utility Judgment Framework via LLMs Inspired by Relevance in Philosophy—0
Fine-Tuning or Fine-Failing? Debunking Performance Myths in Large Language Models—0
Refiner: Restructure Retrieval Content Efficiently to Advance Question-Answering CapabilitiesCode0
R-Eval: A Unified Toolkit for Evaluating Domain Knowledge of Retrieval Augmented Large Language ModelsCode1
Vul-RAG: Enhancing LLM-based Vulnerability Detection via Knowledge-level RAG—0
Evaluating the Efficacy of Open-Source LLMs in Enterprise-Specific RAG Systems: A Comparative Study of Performance and ScalabilityCode0
Current state of LLM Risks and AI Guardrails—0
RAEmoLLM: Retrieval Augmented LLMs for Cross-Domain Misinformation Detection Using In-Context Learning based on Emotional InformationCode0
Automating Pharmacovigilance Evidence Generation: Using Large Language Models to Produce Context-Aware SQL—0
ClimRetrieve: A Benchmarking Dataset for Information Retrieval from Corporate Climate DisclosuresCode0
HIRO: Hierarchical Information Retrieval OptimizationCode0
We Have a Package for You! A Comprehensive Analysis of Package Hallucinations by Code Generating LLMsCode1
Ad Auctions for LLMs via Retrieval Augmented Generation—0
Exploring Fact Memorization and Style Imitation in LLMs Using QLoRA: An Experimental Study and Quality Assessment Methods—0
Battling Botpoop using GenAI for Higher Education: A Study of a Retrieval Augmented Generation Chatbots Impact on Learning—0
Blowfish: Topological and statistical signatures for quantifying ambiguity in semantic search—0
Leveraging Large Language Models for Web Scraping—0
Beyond Words: On Large Language Models Actionability in Mission-Critical Risk Analysis—0
DR-RAG: Applying Dynamic Document Relevance to Retrieval-Augmented Generation for Question-Answering—0
TelecomRAG: Taming Telecom Standards with Retrieval Augmented Generation and LLMs—0
Scholarly Question Answering using Large Language Models in the NFDI4DataScience GatewayCode0
The Impact of Quantization on Retrieval-Augmented Generation: An Analysis of Small LLMs—0
Evaluating the Retrieval Component in LLM-Based Question Answering Systems—0
UMBRELA: UMbrela is the (Open-Source Reproduction of the) Bing RELevance AssessorCode2
Should We Fine-Tune or RAG? Evaluating Different Techniques to Adapt LLMs for DialogueCode0
DomainRAG: A Chinese Benchmark for Evaluating Domain-specific Retrieval-Augmented GenerationCode1
Machine Against the RAG: Jamming Retrieval-Augmented Generation with Blocker Documents—0
A Review of Prominent Paradigms for LLM-Based Agents: Tool Use (Including RAG), Planning, and Feedback LearningCode3
RE-RAG: Improving Open-Domain QA Performance and Interpretability with Relevance Estimator in Retrieval-Augmented GenerationCode0
Corpus Poisoning via Approximate Greedy Gradient DescentCode0
CRAG -- Comprehensive RAG BenchmarkCode3
Show:102550
← PrevPage 35 of 43Next →

No leaderboard results yet.