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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 1601–1625 of 2111 papers

TitleStatusHype
RAD-Bench: Evaluating Large Language Models Capabilities in Retrieval Augmented DialoguesCode0
VERA: Validation and Enhancement for Retrieval Augmented systems—0
RAG-Modulo: Solving Sequential Tasks using Experience, Critics, and Language Models—0
P-RAG: Progressive Retrieval Augmented Generation For Planning on Embodied Everyday Task—0
SuperCoder2.0: Technical Report on Exploring the feasibility of LLMs as Autonomous Programmer—0
Learning variant product relationship and variation attributes from e-commerce website structures—0
LLMs & XAI for Water Sustainability: Seasonal Water Quality Prediction with LIME Explainable AI and a RAG-based Chatbot for Insights—0
THaMES: An End-to-End Tool for Hallucination Mitigation and Evaluation in Large Language ModelsCode0
Investigating Context-Faithfulness in Large Language Models: The Roles of Memory Strength and Evidence Style—0
SFR-RAG: Towards Contextually Faithful LLMs—0
Lab-AI: Using Retrieval Augmentation to Enhance Language Models for Personalized Lab Test Interpretation in Clinical Medicine—0
Language Models and Retrieval Augmented Generation for Automated Structured Data Extraction from Diagnostic Reports—0
Integrating AI's Carbon Footprint into Risk Management Frameworks: Strategies and Tools for Sustainable Compliance in Banking Sector—0
Language Models "Grok" to Copy—0
Hacking, The Lazy Way: LLM Augmented Pentesting—0
A RAG Approach for Generating Competency Questions in Ontology Engineering—0
Exploring Information Retrieval Landscapes: An Investigation of a Novel Evaluation Techniques and Comparative Document Splitting MethodsCode0
LA-RAG:Enhancing LLM-based ASR Accuracy with Retrieval-Augmented Generation—0
Winning Solution For Meta KDD Cup' 24—0
KodeXv0.1: A Family of State-of-the-Art Financial Large Language Models—0
OmniQuery: Contextually Augmenting Captured Multimodal Memory to Enable Personal Question Answering—0
Unleashing Worms and Extracting Data: Escalating the Outcome of Attacks against RAG-based Inference in Scale and Severity Using JailbreakingCode0
Retro-li: Small-Scale Retrieval Augmented Generation Supporting Noisy Similarity Searches and Domain Shift GeneralizationCode0
On the Vulnerability of Applying Retrieval-Augmented Generation within Knowledge-Intensive Application Domains—0
Enhancing Q&A Text Retrieval with Ranking Models: Benchmarking, fine-tuning and deploying Rerankers for RAG—0
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