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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 1701–1725 of 2111 papers

TitleStatusHype
PennyLang: Pioneering LLM-Based Quantum Code Generation with a Novel PennyLane-Centric Dataset—0
Perception of Knowledge Boundary for Large Language Models through Semi-open-ended Question Answering—0
PERC: Plan-As-Query Example Retrieval for Underrepresented Code Generation—0
WeQA: A Benchmark for Retrieval Augmented Generation in Wind Energy Domain—0
PersianRAG: A Retrieval-Augmented Generation System for Persian Language—0
PersonaAI: Leveraging Retrieval-Augmented Generation and Personalized Context for AI-Driven Digital Avatars—0
Personalization Toolkit: Training Free Personalization of Large Vision Language Models—0
Personalized Education with Generative AI and Digital Twins: VR, RAG, and Zero-Shot Sentiment Analysis for Industry 4.0 Workforce Development—0
Personalized Text Generation with Contrastive Activation Steering—0
Personalizing Student-Agent Interactions Using Log-Contextualized Retrieval Augmented Generation (RAG)—0
Phantom: General Trigger Attacks on Retrieval Augmented Language Generation—0
PipeRAG: Fast Retrieval-Augmented Generation via Algorithm-System Co-design—0
Pirates of the RAG: Adaptively Attacking LLMs to Leak Knowledge Bases—0
PISCO: Pretty Simple Compression for Retrieval-Augmented Generation—0
Pistis-RAG: Enhancing Retrieval-Augmented Generation with Human Feedback—0
PlanRAG: Planning-guided Retrieval Augmented Generation—0
Plan with Code: Comparing approaches for robust NL to DSL generation—0
Poisoned LangChain: Jailbreak LLMs by LangChain—0
Poisoned-MRAG: Knowledge Poisoning Attacks to Multimodal Retrieval Augmented Generation—0
Political Events using RAG with LLMs—0
POLYRAG: Integrating Polyviews into Retrieval-Augmented Generation for Medical Applications—0
Poly-Vector Retrieval: Reference and Content Embeddings for Legal Documents—0
Position Engineering: Boosting Large Language Models through Positional Information Manipulation—0
Post-training an LLM for RAG? Train on Self-Generated Demonstrations—0
Practical Design and Benchmarking of Generative AI Applications for Surgical Billing and Coding—0
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