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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 1376–1400 of 2111 papers

TitleStatusHype
Efficient In-Domain Question Answering for Resource-Constrained Environments—0
LLaMa-SciQ: An Educational Chatbot for Answering Science MCQ—0
Evaluating and Enhancing Large Language Models for Novelty Assessment in Scholarly PublicationsCode0
Lighter And Better: Towards Flexible Context Adaptation For Retrieval Augmented Generation—0
Controlling Risk of Retrieval-augmented Generation: A Counterfactual Prompting FrameworkCode0
IRSC: A Zero-shot Evaluation Benchmark for Information Retrieval through Semantic Comprehension in Retrieval-Augmented Generation ScenariosCode0
SwiftDossier: Tailored Automatic Dossier for Drug Discovery with LLMs and Agents—0
Cyber Knowledge Completion Using Large Language Models—0
RAMBO: Enhancing RAG-based Repository-Level Method Body CompletionCode1
Scideator: Human-LLM Scientific Idea Generation Grounded in Research-Paper Facet Recombination—0
Learning When to Retrieve, What to Rewrite, and How to Respond in Conversational QA—0
GEM-RAG: Graphical Eigen Memories For Retrieval Augmented Generation—0
Enhancing Scientific Reproducibility Through Automated BioCompute Object Creation Using Retrieval-Augmented Generation from Publications—0
Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely—0
Lessons Learned on Information Retrieval in Electronic Health Records: A Comparison of Embedding Models and Pooling Strategies—0
Generative AI Is Not Ready for Clinical Use in Patient Education for Lower Back Pain Patients, Even With Retrieval-Augmented Generation—0
Beyond Words: Evaluating Large Language Models in Transportation Planning—0
SURf: Teaching Large Vision-Language Models to Selectively Utilize Retrieved InformationCode1
SMART-RAG: Selection using Determinantal Matrices for Augmented Retrieval—0
AI Assistants for Spaceflight Procedures: Combining Generative Pre-Trained Transformer and Retrieval-Augmented Generation on Knowledge Graphs With Augmented Reality Cues—0
Knowledge in Triples for LLMs: Enhancing Table QA Accuracy with Semantic Extraction—0
QMOS: Enhancing LLMs for Telecommunication with Question Masked loss and Option ShufflingCode0
ShizishanGPT: An Agricultural Large Language Model Integrating Tools and ResourcesCode1
Contextual Compression in Retrieval-Augmented Generation for Large Language Models: A SurveyCode1
Enhancing Large Language Models with Domain-specific Retrieval Augment Generation: A Case Study on Long-form Consumer Health Question Answering in Ophthalmology—0
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