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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 1776–1800 of 2111 papers

TitleStatusHype
Automated C/C++ Program Repair for High-Level Synthesis via Large Language Models—0
DSLR: Document Refinement with Sentence-Level Re-ranking and Reconstruction to Enhance Retrieval-Augmented Generation—0
A Comparative Study of DSL Code Generation: Fine-Tuning vs. Optimized Retrieval Augmentation—0
Why does in-context learning fail sometimes? Evaluating in-context learning on open and closed questionsCode0
RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMs—0
Exploring Advanced Large Language Models with LLMsuite—0
SecGenAI: Enhancing Security of Cloud-based Generative AI Applications within Australian Critical Technologies of National Interest—0
Hybrid RAG-empowered Multi-modal LLM for Secure Data Management in Internet of Medical Things: A Diffusion-based Contract Approach—0
Face4RAG: Factual Consistency Evaluation for Retrieval Augmented Generation in Chinese—0
Learning to Explore and Select for Coverage-Conditioned Retrieval-Augmented GenerationCode0
Ground Every Sentence: Improving Retrieval-Augmented LLMs with Interleaved Reference-Claim Generation—0
Memory^3: Language Modeling with Explicit Memory—0
Large Language Models Struggle in Token-Level Clinical Named Entity RecognitionCode0
From RAG to RICHES: Retrieval Interlaced with Sequence Generation—0
Answering real-world clinical questions using large language model based systems—0
BioKGBench: A Knowledge Graph Checking Benchmark of AI Agent for Biomedical ScienceCode0
SK-VQA: Synthetic Knowledge Generation at Scale for Training Context-Augmented Multimodal LLMs—0
LLM4DESIGN: An Automated Multi-Modal System for Architectural and Environmental Design—0
Development and Evaluation of a Retrieval-Augmented Generation Tool for Creating SAPPhIRE Models of Artificial Systems—0
AutoRAG-HP: Automatic Online Hyper-Parameter Tuning for Retrieval-Augmented Generation—0
AutoPureData: Automated Filtering of Undesirable Web Data to Update LLM KnowledgeCode0
Generating Is Believing: Membership Inference Attacks against Retrieval-Augmented Generation—0
Which Neurons Matter in IR? Applying Integrated Gradients-based Methods to Understand Cross-Encoders—0
RAVEN: Multitask Retrieval Augmented Vision-Language Learning—0
Poisoned LangChain: Jailbreak LLMs by LangChain—0
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