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 1501–1550 of 2111 papers

TitleStatusHype
ReDeEP: Detecting Hallucination in Retrieval-Augmented Generation via Mechanistic Interpretability—0
Parenting: Optimizing Knowledge Selection of Retrieval-Augmented Language Models with Parameter Decoupling and Tailored Tuning—0
FunnelRAG: A Coarse-to-Fine Progressive Retrieval Paradigm for RAG—0
Enhancing Retrieval-Augmented Audio Captioning with Generation-Assisted Multimodal Querying and Progressive Learning—0
Model-based Large Language Model Customization as Service—0
STACKFEED: Structured Textual Actor-Critic Knowledge Base Editing with FeedBack—0
Beyond-RAG: Question Identification and Answer Generation in Real-Time Conversations—0
Honest AI: Fine-Tuning "Small" Language Models to Say "I Don't Know", and Reducing Hallucination in RAG—0
A Comparative Study of PDF Parsing Tools Across Diverse Document Categories—0
Retrieval Instead of Fine-tuning: A Retrieval-based Parameter Ensemble for Zero-shot Learning—0
Learning to Rank for Multiple Retrieval-Augmented Models through Iterative Utility Maximization—0
Synthetic Knowledge Ingestion: Towards Knowledge Refinement and Injection for Enhancing Large Language ModelsCode0
Quebec Automobile Insurance Question-Answering With Retrieval-Augmented GenerationCode0
Developing a Pragmatic Benchmark for Assessing Korean Legal Language Understanding in Large Language ModelsCode0
Enhancing Long Context Performance in LLMs Through Inner Loop Query Mechanism—0
oRetrieval Augmented Generation for 10 Large Language Models and its Generalizability in Assessing Medical Fitness—0
A Methodology for Evaluating RAG Systems: A Case Study On Configuration Dependency ValidationCode0
News Reporter: A Multi-lingual LLM Framework for Broadcast T.V News—0
KRAG Framework for Enhancing LLMs in the Legal Domain—0
Do You Know What You Are Talking About? Characterizing Query-Knowledge Relevance For Reliable Retrieval Augmented Generation—0
No Free Lunch: Retrieval-Augmented Generation Undermines Fairness in LLMs, Even for Vigilant Users—0
Increasing the Difficulty of Automatically Generated Questions via Reinforcement Learning with Synthetic Preference—0
Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models—0
Context-Augmented Code Generation Using Programming Knowledge Graphs—0
Exploring the Meaningfulness of Nearest Neighbor Search in High-Dimensional Space—0
Long-Context LLMs Meet RAG: Overcoming Challenges for Long Inputs in RAG—0
Retrieving, Rethinking and Revising: The Chain-of-Verification Can Improve Retrieval Augmented Generation—0
ENWAR: A RAG-empowered Multi-Modal LLM Framework for Wireless Environment Perception—0
Improving Embedding Accuracy for Document Retrieval Using Entity Relationship Maps and Model-Aware Contrastive Sampling—0
Fortify Your Foundations: Practical Privacy and Security for Foundation Model Deployments In The Cloud—0
Automating Bibliometric Analysis with Sentence Transformers and Retrieval-Augmented Generation (RAG): A Pilot Study in Semantic and Contextual Search for Customized Literature Characterization for High-Impact Urban Research—0
Application of NotebookLM, a Large Language Model with Retrieval-Augmented Generation, for Lung Cancer Staging—0
Driving with Regulation: Interpretable Decision-Making for Autonomous Vehicles with Retrieval-Augmented Reasoning via LLM—0
GARLIC: LLM-Guided Dynamic Progress Control with Hierarchical Weighted Graph for Long Document QA—0
Deciphering the Interplay of Parametric and Non-parametric Memory in Retrieval-augmented Language ModelsCode0
LLaVA Needs More Knowledge: Retrieval Augmented Natural Language Generation with Knowledge Graph for Explaining Thoracic PathologiesCode0
Knowledge Graph Based Agent for Complex, Knowledge-Intensive QA in Medicine—0
TableRAG: Million-Token Table Understanding with Language ModelsCode0
Inference Scaling for Long-Context Retrieval Augmented Generation—0
MindScope: Exploring cognitive biases in large language models through Multi-Agent SystemsCode0
Consistent Autoformalization for Constructing Mathematical LibrariesCode0
Metadata-based Data Exploration with Retrieval-Augmented Generation for Large Language Models—0
Assessing the Performance of Human-Capable LLMs -- Are LLMs Coming for Your Job?—0
Auto-GDA: Automatic Domain Adaptation for Efficient Grounding Verification in Retrieval Augmented Generation—0
ORAssistant: A Custom RAG-based Conversational Assistant for OpenROAD—0
Ward: Provable RAG Dataset Inference via LLM Watermarks—0
Scalable Frame-based Construction of Sociocultural NormBases for Socially-Aware Dialogues—0
A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions—0
UncertaintyRAG: Span-Level Uncertainty Enhanced Long-Context Modeling for Retrieval-Augmented Generation—0
Intrinsic Evaluation of RAG Systems for Deep-Logic Questions—0
Show:102550
← PrevPage 31 of 43Next →

No leaderboard results yet.