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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 1976–2000 of 2111 papers

TitleStatusHype
Where is the answer? Investigating Positional Bias in Language Model Knowledge ExtractionCode0
AI-TA: Towards an Intelligent Question-Answer Teaching Assistant using Open-Source LLMsCode0
R-Search: Empowering LLM Reasoning with Search via Multi-Reward Reinforcement LearningCode0
Who's Who: Large Language Models Meet Knowledge Conflicts in PracticeCode0
QPaug: Question and Passage Augmentation for Open-Domain Question Answering of LLMsCode0
K-COMP: Retrieval-Augmented Medical Domain Question Answering With Knowledge-Injected CompressorCode0
KBAlign: Efficient Self Adaptation on Specific Knowledge BasesCode0
JMLR: Joint Medical LLM and Retrieval Training for Enhancing Reasoning and Professional Question Answering CapabilityCode0
CBM-RAG: Demonstrating Enhanced Interpretability in Radiology Report Generation with Multi-Agent RAG and Concept Bottleneck ModelsCode0
IRSC: A Zero-shot Evaluation Benchmark for Information Retrieval through Semantic Comprehension in Retrieval-Augmented Generation ScenariosCode0
RuCCoD: Towards Automated ICD Coding in RussianCode0
Does Context Matter? ContextualJudgeBench for Evaluating LLM-based Judges in Contextual SettingsCode0
Document Haystacks: Vision-Language Reasoning Over Piles of 1000+ DocumentsCode0
RACCOON: A Retrieval-Augmented Generation Approach for Location Coordinate Capture from News ArticlesCode0
RAC: Efficient LLM Factuality Correction with Retrieval AugmentationCode0
RAD-Bench: Evaluating Large Language Models Capabilities in Retrieval Augmented DialoguesCode0
RadioRAG: Factual large language models for enhanced diagnostics in radiology using online retrieval augmented generationCode0
RustEvo^2: An Evolving Benchmark for API Evolution in LLM-based Rust Code GenerationCode0
RAEmoLLM: Retrieval Augmented LLMs for Cross-Domain Misinformation Detection Using In-Context Learning based on Emotional InformationCode0
DNAHLM -- DNA sequence and Human Language mixed large language ModelCode0
RAFT: Adapting Language Model to Domain Specific RAGCode0
DIRAS: Efficient LLM Annotation of Document Relevance in Retrieval Augmented GenerationCode0
Walk&Retrieve: Simple Yet Effective Zero-shot Retrieval-Augmented Generation via Knowledge Graph WalksCode0
Investigating the performance of Retrieval-Augmented Generation and fine-tuning for the development of AI-driven knowledge-based systemsCode0
Developing Visual Augmented Q&A System using Scalable Vision Embedding Retrieval & Late Interaction Re-rankerCode0
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