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 651–675 of 2111 papers

TitleStatusHype
A GEN AI Framework for Medical Note Generation—0
Accelerating Causal Network Discovery of Alzheimer Disease Biomarkers via Scientific Literature-based Retrieval Augmented Generation—0
DFA-RAG: Conversational Semantic Router for Large Language Model with Definite Finite Automaton—0
Chatbot Arena Meets Nuggets: Towards Explanations and Diagnostics in the Evaluation of LLM Responses—0
A Reasoning-Focused Legal Retrieval Benchmark—0
After Retrieval, Before Generation: Enhancing the Trustworthiness of Large Language Models in RAG—0
Exploring Fact Memorization and Style Imitation in LLMs Using QLoRA: An Experimental Study and Quality Assessment Methods—0
Exploring Knowledge Boundaries in Large Language Models for Retrieval Judgment—0
Exploring the Impact of Table-to-Text Methods on Augmenting LLM-based Question Answering with Domain Hybrid Data—0
Face4RAG: Factual Consistency Evaluation for Retrieval Augmented Generation in Chinese—0
FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation—0
Characterizing the Dilemma of Performance and Index Size in Billion-Scale Vector Search and Breaking It with Second-Tier Memory—0
Characterizing Network Structure of Anti-Trans Actors on TikTok—0
Chain-of-Thought Poisoning Attacks against R1-based Retrieval-Augmented Generation Systems—0
Chain-of-Retrieval Augmented Generation—0
ARCS: Agentic Retrieval-Augmented Code Synthesis with Iterative Refinement—0
A Fine-tuning Enhanced RAG System with Quantized Influence Measure as AI Judge—0
Chain-of-Rank: Enhancing Large Language Models for Domain-Specific RAG in Edge Device—0
Chain of Agents: Large Language Models Collaborating on Long-Context Tasks—0
ArchRAG: Attributed Community-based Hierarchical Retrieval-Augmented Generation—0
CG-RAG: Research Question Answering by Citation Graph Retrieval-Augmented LLMs—0
ARCeR: an Agentic RAG for the Automated Definition of Cyber Ranges—0
Accelerating Adaptive Retrieval Augmented Generation via Instruction-Driven Representation Reduction of Retrieval Overlaps—0
C-FedRAG: A Confidential Federated Retrieval-Augmented Generation System—0
CCSK:Cognitive Convection of Self-Knowledge Based Retrieval Augmentation for Large Language Models—0
Show:102550
← PrevPage 27 of 85Next →

No leaderboard results yet.