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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

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Showing 1801–1825 of 2111 papers

TitleStatusHype
RAG vs. GraphRAG: A Systematic Evaluation and Key Insights—0
Examining Long-Context Large Language Models for Environmental Review Document Comprehension—0
RAG Without the Lag: Interactive Debugging for Retrieval-Augmented Generation Pipelines—0
RAG-WM: An Efficient Black-Box Watermarking Approach for Retrieval-Augmented Generation of Large Language Models—0
RAGXplain: From Explainable Evaluation to Actionable Guidance of RAG Pipelines—0
RAG-Zeval: Towards Robust and Interpretable Evaluation on RAG Responses through End-to-End Rule-Guided Reasoning—0
Raiders of the Lost Dependency: Fixing Dependency Conflicts in Python using LLMs—0
RAMIE: Retrieval-Augmented Multi-task Information Extraction with Large Language Models on Dietary Supplements—0
RAMO: Retrieval-Augmented Generation for Enhancing MOOCs Recommendations—0
RAM: Towards an Ever-Improving Memory System by Learning from Communications—0
Ranking Free RAG: Replacing Re-ranking with Selection in RAG for Sensitive Domains—0
RankLLM: A Python Package for Reranking with LLMs—0
RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMs—0
RAR: Setting Knowledge Tripwires for Retrieval Augmented Rejection—0
RAVEN: Multitask Retrieval Augmented Vision-Language Learning—0
Re2G: Retrieve, Rerank, Generate—0
Reading with Intent—0
Reading with Intent -- Neutralizing Intent—0
NitiBench: A Comprehensive Studies of LLM Frameworks Capabilities for Thai Legal Question AnsweringCode0
Network-informed Prompt Engineering against Organized Astroturf Campaigns under Extreme Class ImbalanceCode0
NeoQA: Evidence-based Question Answering with Generated News EventsCode0
Reference Trustable Decoding: A Training-Free Augmentation Paradigm for Large Language ModelsCode0
CrAM: Credibility-Aware Attention Modification in LLMs for Combating Misinformation in RAGCode0
MuseRAG: Idea Originality Scoring At ScaleCode0
Beyond Benchmarks: Evaluating Embedding Model Similarity for Retrieval Augmented Generation SystemsCode0
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