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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 2001–2025 of 2111 papers

TitleStatusHype
HyperRAG: Enhancing Quality-Efficiency Tradeoffs in Retrieval-Augmented Generation with Reranker KV-Cache Reuse—0
IAG: Induction-Augmented Generation Framework for Answering Reasoning Questions—0
ICLERB: In-Context Learning Embedding and Reranker Benchmark—0
Identifying Performance-Sensitive Configurations in Software Systems through Code Analysis with LLM Agents—0
ImageRAG: Dynamic Image Retrieval for Reference-Guided Image Generation—0
Implementation of a Generative AI Assistant in K-12 Education: The CyberScholar Initiative—0
Improving Assessment of Tutoring Practices using Retrieval-Augmented Generation—0
Improving Embedding Accuracy for Document Retrieval Using Entity Relationship Maps and Model-Aware Contrastive Sampling—0
Improving Factuality of 3D Brain MRI Report Generation with Paired Image-domain Retrieval and Text-domain Augmentation—0
Improving Factuality with Explicit Working Memory—0
Improving Multimodal LLMs Ability In Geometry Problem Solving, Reasoning, And Multistep Scoring—0
Improving RAG Retrieval via Propositional Content Extraction: a Speech Act Theory Approach—0
Improving Retrieval for RAG based Question Answering Models on Financial Documents—0
Improving TCM Question Answering through Tree-Organized Self-Reflective Retrieval with LLMs—0
Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answering—0
Improving the Reliability of LLMs: Combining CoT, RAG, Self-Consistency, and Self-Verification—0
Improving Zero-shot LLM Re-Ranker with Risk Minimization—0
IM-RAG: Multi-Round Retrieval-Augmented Generation Through Learning Inner Monologues—0
Increasing the Difficulty of Automatically Generated Questions via Reinforcement Learning with Synthetic Preference—0
In Defense of RAG in the Era of Long-Context Language Models—0
In-depth Analysis of Graph-based RAG in a Unified Framework—0
Inference Scaled GraphRAG: Improving Multi Hop Question Answering on Knowledge Graphs—0
Inference Scaling for Bridging Retrieval and Augmented Generation—0
Inference Scaling for Long-Context Retrieval Augmented Generation—0
Infinite Retrieval: Attention Enhanced LLMs in Long-Context Processing—0
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