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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
Thread: A Logic-Based Data Organization Paradigm for How-To Question Answering with Retrieval Augmented Generation—0
Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More?Code3
WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia—0
R^2AG: Incorporating Retrieval Information into Retrieval Augmented GenerationCode1
RichRAG: Crafting Rich Responses for Multi-faceted Queries in Retrieval-Augmented Generation—0
Unified Active Retrieval for Retrieval Augmented GenerationCode1
Intermediate Distillation: Data-Efficient Distillation from Black-Box LLMs for Information Retrieval—0
PlanRAG: A Plan-then-Retrieval Augmented Generation for Generative Large Language Models as Decision MakersCode2
Debate as Optimization: Adaptive Conformal Prediction and Diverse Retrieval for Event Extraction—0
From RAGs to rich parameters: Probing how language models utilize external knowledge over parametric information for factual queries—0
Identifying Performance-Sensitive Configurations in Software Systems through Code Analysis with LLM Agents—0
Retrieval-Augmented Generation for Generative Artificial Intelligence in Medicine—0
Query Routing for Homogeneous Tools: An Instantiation in the RAG Scenario—0
Retrieval Meets Reasoning: Dynamic In-Context Editing for Long-Text Understanding—0
Satyrn: A Platform for Analytics Augmented GenerationCode0
SeRTS: Self-Rewarding Tree Search for Biomedical Retrieval-Augmented Generation—0
CrAM: Credibility-Aware Attention Modification in LLMs for Combating Misinformation in RAGCode0
Retrieval-Augmented Feature Generation for Domain-Specific Classification—0
TRACE the Evidence: Constructing Knowledge-Grounded Reasoning Chains for Retrieval-Augmented GenerationCode1
Iterative Utility Judgment Framework via LLMs Inspired by Relevance in Philosophy—0
Fine-Tuning or Fine-Failing? Debunking Performance Myths in Large Language Models—0
Refiner: Restructure Retrieval Content Efficiently to Advance Question-Answering CapabilitiesCode0
R-Eval: A Unified Toolkit for Evaluating Domain Knowledge of Retrieval Augmented Large Language ModelsCode1
Vul-RAG: Enhancing LLM-based Vulnerability Detection via Knowledge-level RAG—0
Evaluating the Efficacy of Open-Source LLMs in Enterprise-Specific RAG Systems: A Comparative Study of Performance and ScalabilityCode0
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