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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
Deficiency of Large Language Models in Finance: An Empirical Examination of Hallucination—0
Dehallucinating Parallel Context Extension for Retrieval-Augmented Generation—0
Developing an Artificial Intelligence Tool for Personalized Breast Cancer Treatment Plans based on the NCCN Guidelines—0
Development and Evaluation of a Retrieval-Augmented Generation Tool for Creating SAPPhIRE Models of Artificial Systems—0
Development and Testing of a Novel Large Language Model-Based Clinical Decision Support Systems for Medication Safety in 12 Clinical Specialties—0
Development and Testing of Retrieval Augmented Generation in Large Language Models -- A Case Study Report—0
Development of a Reliable and Accessible Caregiving Language Model (CaLM)—0
Dewey Long Context Embedding Model: A Technical Report—0
DFIN-SQL: Integrating Focused Schema with DIN-SQL for Superior Accuracy in Large-Scale Databases—0
DGRAG: Distributed Graph-based Retrieval-Augmented Generation in Edge-Cloud Systems—0
DH-RAG: A Dynamic Historical Context-Powered Retrieval-Augmented Generation Method for Multi-Turn Dialogue—0
Diagnosing and Addressing Pitfalls in KG-RAG Datasets: Toward More Reliable Benchmarking—0
Diagnosing and Resolving Cloud Platform Instability with Multi-modal RAG LLMs—0
Dialectical Alignment: Resolving the Tension of 3H and Security Threats of LLMs—0
Dialogic Pedagogy for Large Language Models: Aligning Conversational AI with Proven Theories of Learning—0
Differential Privacy of Cross-Attention with Provable Guarantee—0
Dimensionality Reduction in Sentence Transformer Vector Databases with Fast Fourier Transform—0
DioR: Adaptive Cognitive Detection and Contextual Retrieval Optimization for Dynamic Retrieval-Augmented Generation—0
Distill-SynthKG: Distilling Knowledge Graph Synthesis Workflow for Improved Coverage and Efficiency—0
Diversify-verify-adapt: Efficient and Robust Retrieval-Augmented Ambiguous Question Answering—0
Diversity Enhances an LLM's Performance in RAG and Long-context Task—0
Divide by Question, Conquer by Agent: SPLIT-RAG with Question-Driven Graph Partitioning—0
DMQR-RAG: Diverse Multi-Query Rewriting for RAG—0
DocReRank: Single-Page Hard Negative Query Generation for Training Multi-Modal RAG Rerankers—0
DoctorRAG: Medical RAG Fusing Knowledge with Patient Analogy through Textual Gradients—0
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