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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 1001–1025 of 2111 papers

TitleStatusHype
Leveraging Retrieval-Augmented Generation and Large Language Models to Predict SERCA-Binding Protein Fragments from Cardiac Proteomics Data—0
Efficient Federated Search for Retrieval-Augmented Generation—0
Automated Code Generation and Validation for Software Components of Microcontrollers—0
Say Less, Mean More: Leveraging Pragmatics in Retrieval-Augmented Generation—0
Faster, Cheaper, Better: Multi-Objective Hyperparameter Optimization for LLM and RAG Systems—0
Detecting Knowledge Boundary of Vision Large Language Models by Sampling-Based InferenceCode0
Language Model Re-rankers are Steered by Lexical Similarities—0
A Hybrid Approach to Information Retrieval and Answer Generation for Regulatory TextsCode0
Graphy'our Data: Towards End-to-End Modeling, Exploring and Generating Report from Raw Data—0
Evaluating the Effect of Retrieval Augmentation on Social Biases—0
Mitigating Bias in RAG: Controlling the EmbedderCode0
MEMERAG: A Multilingual End-to-End Meta-Evaluation Benchmark for Retrieval Augmented GenerationCode0
Optimizing Retrieval-Augmented Generation of Medical Content for Spaced Repetition Learning—0
LawPal : A Retrieval Augmented Generation Based System for Enhanced Legal Accessibility in India—0
Retrieval-Augmented Visual Question Answering via Built-in Autoregressive Search Engines—0
D2S-FLOW: Automated Parameter Extraction from Datasheets for SPICE Model Generation Using Large Language Models—0
Visual-RAG: Benchmarking Text-to-Image Retrieval Augmented Generation for Visual Knowledge Intensive QueriesCode0
RAG-Enhanced Collaborative LLM Agents for Drug Discovery—0
An Autonomous Network Orchestration Framework Integrating Large Language Models with Continual Reinforcement Learning—0
Worse than Zero-shot? A Fact-Checking Dataset for Evaluating the Robustness of RAG Against Misleading Retrievals—0
Cross-Format Retrieval-Augmented Generation in XR with LLMs for Context-Aware Maintenance Assistance—0
From Documents to Dialogue: Building KG-RAG Enhanced AI Assistants—0
Retrieval-Augmented Speech Recognition Approach for Domain Challenges—0
Chats-Grid: An Iterative Retrieval Q&A Optimization Scheme Leveraging Large Model and Retrieval Enhancement Generation in smart grid—0
Chain-of-Rank: Enhancing Large Language Models for Domain-Specific RAG in Edge Device—0
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