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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 1551–1600 of 2111 papers

TitleStatusHype
How Much Can RAG Help the Reasoning of LLM?—0
Domain-Specific Retrieval-Augmented Generation Using Vector Stores, Knowledge Graphs, and Tensor Factorization—0
Reward-RAG: Enhancing RAG with Reward Driven Supervision—0
Can We Further Elicit Reasoning in LLMs? Critic-Guided Planning with Retrieval-Augmentation for Solving Challenging Tasks—0
Enhancing Retrieval in QA Systems with Derived Feature AssociationCode0
Segment as You Wish -- Free-Form Language-Based Segmentation for Medical ImagesCode0
BordIRlines: A Dataset for Evaluating Cross-lingual Retrieval-Augmented GenerationCode0
Quantifying reliance on external information over parametric knowledge during Retrieval Augmented Generation (RAG) using mechanistic analysis—0
Classifying Peace in Global Media Using RAG and Intergroup Reciprocity—0
Boosting the Capabilities of Compact Models in Low-Data Contexts with Large Language Models and Retrieval-Augmented Generation—0
Optimizing and Evaluating Enterprise Retrieval-Augmented Generation (RAG): A Content Design PerspectiveCode0
LLM Hallucinations in Practical Code Generation: Phenomena, Mechanism, and MitigationCode0
Ingest-And-Ground: Dispelling Hallucinations from Continually-Pretrained LLMs with RAG—0
Reference Trustable Decoding: A Training-Free Augmentation Paradigm for Large Language ModelsCode0
BSharedRAG: Backbone Shared Retrieval-Augmented Generation for the E-commerce Domain—0
Beyond Scores: A Modular RAG-Based System for Automatic Short Answer Scoring with FeedbackCode0
Does RAG Introduce Unfairness in LLMs? Evaluating Fairness in Retrieval-Augmented Generation SystemsCode0
PEAR: Position-Embedding-Agnostic Attention Re-weighting Enhances Retrieval-Augmented Generation with Zero Inference Overhead—0
Crafting Personalized Agents through Retrieval-Augmented Generation on Editable Memory Graphs—0
HealthQ: Unveiling Questioning Capabilities of LLM Chains in Healthcare Conversations—0
AIPatient: Simulating Patients with EHRs and LLM Powered Agentic Workflow—0
A GEN AI Framework for Medical Note Generation—0
Corpus-informed Retrieval Augmented Generation of Clarifying Questions—0
Embodied-RAG: General Non-parametric Embodied Memory for Retrieval and Generation—0
Enhancing Tourism Recommender Systems for Sustainable City Trips Using Retrieval-Augmented Generation—0
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
Cyber Knowledge Completion Using Large Language Models—0
Controlling Risk of Retrieval-augmented Generation: A Counterfactual Prompting FrameworkCode0
SwiftDossier: Tailored Automatic Dossier for Drug Discovery with LLMs and Agents—0
IRSC: A Zero-shot Evaluation Benchmark for Information Retrieval through Semantic Comprehension in Retrieval-Augmented Generation ScenariosCode0
Lighter And Better: Towards Flexible Context Adaptation For Retrieval Augmented Generation—0
Enhancing Scientific Reproducibility Through Automated BioCompute Object Creation Using Retrieval-Augmented Generation from Publications—0
Scideator: Human-LLM Scientific Idea Generation Grounded in Research-Paper Facet Recombination—0
Generative AI Is Not Ready for Clinical Use in Patient Education for Lower Back Pain Patients, Even With Retrieval-Augmented Generation—0
Learning When to Retrieve, What to Rewrite, and How to Respond in Conversational QA—0
Lessons Learned on Information Retrieval in Electronic Health Records: A Comparison of Embedding Models and Pooling Strategies—0
Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely—0
GEM-RAG: Graphical Eigen Memories For Retrieval Augmented Generation—0
Beyond Words: Evaluating Large Language Models in Transportation Planning—0
QMOS: Enhancing LLMs for Telecommunication with Question Masked loss and Option ShufflingCode0
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
SMART-RAG: Selection using Determinantal Matrices for Augmented Retrieval—0
Enhancing Large Language Models with Domain-specific Retrieval Augment Generation: A Case Study on Long-form Consumer Health Question Answering in Ophthalmology—0
Retrieval-Augmented Test Generation: How Far Are We?—0
Enhancing E-commerce Product Title Translation with Retrieval-Augmented Generation and Large Language Models—0
Should RAG Chatbots Forget Unimportant Conversations? Exploring Importance and Forgetting with Psychological InsightsCode0
A New Perspective on ADHD Research: Knowledge Graph Construction with LLMs and Network Based InsightsCode0
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