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 1776–1800 of 2111 papers

TitleStatusHype
RAGDiffusion: Faithful Cloth Generation via External Knowledge Assimilation—0
RAG Does Not Work for Enterprises—0
Natural Language Interaction with a Household Electricity Knowledge-based Digital Twin—0
RAG-Enhanced Collaborative LLM Agents for Drug Discovery—0
RAG+: Enhancing Retrieval-Augmented Generation with Application-Aware Reasoning—0
RAG for Effective Supply Chain Security Questionnaire Automation—0
RAG-Fusion: a New Take on Retrieval-Augmented Generation—0
RAGged Edges: The Double-Edged Sword of Retrieval-Augmented Chatbots—0
RAG-Gym: Optimizing Reasoning and Search Agents with Process Supervision—0
RAG-KG-IL: A Multi-Agent Hybrid Framework for Reducing Hallucinations and Enhancing LLM Reasoning through RAG and Incremental Knowledge Graph Learning Integration—0
RAG/LLM Augmented Switching Driven Polymorphic Metaheuristic Framework—0
RAG LLMs are Not Safer: A Safety Analysis of Retrieval-Augmented Generation for Large Language Models—0
RAG-MCP: Mitigating Prompt Bloat in LLM Tool Selection via Retrieval-Augmented Generation—0
RAG-Modulo: Solving Sequential Tasks using Experience, Critics, and Language Models—0
RAGONITE: Iterative Retrieval on Induced Databases and Verbalized RDF for Conversational QA over KGs with RAG—0
RAG-Optimized Tibetan Tourism LLMs: Enhancing Accuracy and Personalization—0
RAG-Reward: Optimizing RAG with Reward Modeling and RLHF—0
RAG-RL: Advancing Retrieval-Augmented Generation via RL and Curriculum Learning—0
RAGServe: Fast Quality-Aware RAG Systems with Configuration Adaptation—0
RAG-Star: Enhancing Deliberative Reasoning with Retrieval Augmented Verification and Refinement—0
RAGSys: Item-Cold-Start Recommender as RAG System—0
RAGtifier: Evaluating RAG Generation Approaches of State-of-the-Art RAG Systems for the SIGIR LiveRAG Competition—0
RAGulator: Lightweight Out-of-Context Detectors for Grounded Text Generation—0
RAG-Verus: Repository-Level Program Verification with LLMs using Retrieval Augmented Generation—0
RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture—0
Show:102550
← PrevPage 72 of 85Next →

No leaderboard results yet.